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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Verga, P"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.11900">arXiv:2410.11900</a> <span> [<a href="https://arxiv.org/pdf/2410.11900">pdf</a>, <a href="https://arxiv.org/format/2410.11900">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> </div> </div> <p class="title is-5 mathjax"> FLARE: Faithful Logic-Aided Reasoning and Exploration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Arakelyan%2C+E">Erik Arakelyan</a>, <a href="/search/cs?searchtype=author&query=Minervini%2C+P">Pasquale Minervini</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=Lewis%2C+P">Patrick Lewis</a>, <a href="/search/cs?searchtype=author&query=Augenstein%2C+I">Isabelle Augenstein</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.11900v4-abstract-short" style="display: inline;"> Modern Question Answering (QA) and Reasoning approaches based on Large Language Models (LLMs) commonly use prompting techniques, such as Chain-of-Thought (CoT), assuming the resulting generation will have a more granular exploration and reasoning over the question space and scope. However, such methods struggle with generating outputs that are faithful to the intermediate chain of reasoning produc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.11900v4-abstract-full').style.display = 'inline'; document.getElementById('2410.11900v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.11900v4-abstract-full" style="display: none;"> Modern Question Answering (QA) and Reasoning approaches based on Large Language Models (LLMs) commonly use prompting techniques, such as Chain-of-Thought (CoT), assuming the resulting generation will have a more granular exploration and reasoning over the question space and scope. However, such methods struggle with generating outputs that are faithful to the intermediate chain of reasoning produced by the model. On the other end of the spectrum, neuro-symbolic methods such as Faithful CoT (F-CoT) propose to combine LLMs with external symbolic solvers. While such approaches boast a high degree of faithfulness, they usually require a model trained for code generation and struggle with tasks that are ambiguous or hard to formalise strictly. We introduce $\textbf{F}$aithful $\textbf{L}$ogic-$\textbf{A}$ided $\textbf{R}$easoning and $\textbf{E}$xploration ($\textbf{FLARE}$), a novel interpretable approach for traversing the problem space using task decompositions. We use the LLM to plan a solution, soft-formalise the query into facts and predicates using a logic programming code and simulate that code execution using an exhaustive multi-hop search over the defined space. Our method allows us to compute the faithfulness of the reasoning process w.r.t. the generated code and analyse the steps of the multi-hop search without relying on external solvers. Our methods achieve SOTA results on $\mathbf{7}$ out of $\mathbf{9}$ diverse reasoning benchmarks. We also show that model faithfulness positively correlates with overall performance and further demonstrate that $\textbf{FLARE}$ allows pinpointing the decisive factors sufficient for and leading to the correct answer with optimal reasoning during the multi-hop search. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.11900v4-abstract-full').style.display = 'none'; document.getElementById('2410.11900v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.03227">arXiv:2410.03227</a> <span> [<a href="https://arxiv.org/pdf/2410.03227">pdf</a>, <a href="https://arxiv.org/format/2410.03227">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> ALR$^2$: A Retrieve-then-Reason Framework for Long-context Question Answering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+H">Huayang Li</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=Sen%2C+P">Priyanka Sen</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+B">Bowen Yang</a>, <a href="/search/cs?searchtype=author&query=Viswanathan%2C+V">Vijay Viswanathan</a>, <a href="/search/cs?searchtype=author&query=Lewis%2C+P">Patrick Lewis</a>, <a href="/search/cs?searchtype=author&query=Watanabe%2C+T">Taro Watanabe</a>, <a href="/search/cs?searchtype=author&query=Su%2C+Y">Yixuan Su</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.03227v1-abstract-short" style="display: inline;"> The context window of large language models (LLMs) has been extended significantly in recent years. However, while the context length that the LLM can process has grown, the capability of the model to accurately reason over that context degrades noticeably. This occurs because modern LLMs often become overwhelmed by the vast amount of information in the context; when answering questions, the model… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03227v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03227v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03227v1-abstract-full" style="display: none;"> The context window of large language models (LLMs) has been extended significantly in recent years. However, while the context length that the LLM can process has grown, the capability of the model to accurately reason over that context degrades noticeably. This occurs because modern LLMs often become overwhelmed by the vast amount of information in the context; when answering questions, the model must identify and reason over relevant evidence sparsely distributed throughout the text. To alleviate the challenge of long-context reasoning, we develop a retrieve-then-reason framework, enabling LLMs to reason over relevant evidence collected during an intermediate retrieval step. We find that modern LLMs struggle to accurately retrieve relevant facts and instead, often hallucinate "retrieved facts", resulting in flawed reasoning and the production of incorrect answers. To address these issues, we introduce ALR$^2$, a method that augments the long-context reasoning capability of LLMs via an explicit two-stage procedure, i.e., aligning LLMs with the objectives of both retrieval and reasoning. We demonstrate the efficacy of ALR$^2$ for mitigating performance degradation in long-context reasoning tasks. Through extensive experiments on long-context QA benchmarks, we find our method to outperform competitive baselines by large margins, achieving at least 8.4 and 7.9 EM gains on the long-context versions of HotpotQA and SQuAD datasets, respectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03227v1-abstract-full').style.display = 'none'; document.getElementById('2410.03227v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.14960">arXiv:2408.14960</a> <span> [<a href="https://arxiv.org/pdf/2408.14960">pdf</a>, <a href="https://arxiv.org/format/2408.14960">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Odumakinde%2C+A">Ayomide Odumakinde</a>, <a href="/search/cs?searchtype=author&query=D%27souza%2C+D">Daniel D'souza</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=Ermis%2C+B">Beyza Ermis</a>, <a href="/search/cs?searchtype=author&query=Hooker%2C+S">Sara Hooker</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.14960v1-abstract-short" style="display: inline;"> The use of synthetic data has played a critical role in recent state-of-art breakthroughs. However, overly relying on a single oracle teacher model to generate data has been shown to lead to model collapse and invite propagation of biases. These limitations are particularly evident in multilingual settings, where the absence of a universally effective teacher model that excels across all languages… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.14960v1-abstract-full').style.display = 'inline'; document.getElementById('2408.14960v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.14960v1-abstract-full" style="display: none;"> The use of synthetic data has played a critical role in recent state-of-art breakthroughs. However, overly relying on a single oracle teacher model to generate data has been shown to lead to model collapse and invite propagation of biases. These limitations are particularly evident in multilingual settings, where the absence of a universally effective teacher model that excels across all languages presents significant challenges. In this work, we address these extreme difference by introducing "multilingual arbitrage", which capitalizes on performance variations between multiple models for a given language. To do so, we strategically route samples through a diverse pool of models, each with unique strengths in different languages. Across exhaustive experiments on state-of-art models, our work suggests that arbitrage techniques allow for spectacular gains in performance that far outperform relying on a single teacher. In particular, compared to the best single teacher, we observe gains of up to 56.5% improvement in win rates averaged across all languages when switching to multilingual arbitrage. We observe the most significant gains for the least resourced languages in our pool. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.14960v1-abstract-full').style.display = 'none'; document.getElementById('2408.14960v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.18796">arXiv:2404.18796</a> <span> [<a href="https://arxiv.org/pdf/2404.18796">pdf</a>, <a href="https://arxiv.org/format/2404.18796">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=Hofstatter%2C+S">Sebastian Hofstatter</a>, <a href="/search/cs?searchtype=author&query=Althammer%2C+S">Sophia Althammer</a>, <a href="/search/cs?searchtype=author&query=Su%2C+Y">Yixuan Su</a>, <a href="/search/cs?searchtype=author&query=Piktus%2C+A">Aleksandra Piktus</a>, <a href="/search/cs?searchtype=author&query=Arkhangorodsky%2C+A">Arkady Arkhangorodsky</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+M">Minjie Xu</a>, <a href="/search/cs?searchtype=author&query=White%2C+N">Naomi White</a>, <a href="/search/cs?searchtype=author&query=Lewis%2C+P">Patrick Lewis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.18796v2-abstract-short" style="display: inline;"> As Large Language Models (LLMs) have become more advanced, they have outpaced our abilities to accurately evaluate their quality. Not only is finding data to adequately probe particular model properties difficult, but evaluating the correctness of a model's freeform generation alone is a challenge. To address this, many evaluations now rely on using LLMs themselves as judges to score the quality o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.18796v2-abstract-full').style.display = 'inline'; document.getElementById('2404.18796v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.18796v2-abstract-full" style="display: none;"> As Large Language Models (LLMs) have become more advanced, they have outpaced our abilities to accurately evaluate their quality. Not only is finding data to adequately probe particular model properties difficult, but evaluating the correctness of a model's freeform generation alone is a challenge. To address this, many evaluations now rely on using LLMs themselves as judges to score the quality of outputs from other LLMs. Evaluations most commonly use a single large model like GPT4. While this method has grown in popularity, it is costly, has been shown to introduce intramodel bias, and in this work, we find that very large models are often unnecessary. We propose instead to evaluate models using a Panel of LLm evaluators (PoLL). Across three distinct judge settings and spanning six different datasets, we find that using a PoLL composed of a larger number of smaller models outperforms a single large judge, exhibits less intra-model bias due to its composition of disjoint model families, and does so while being over seven times less expensive. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.18796v2-abstract-full').style.display = 'none'; document.getElementById('2404.18796v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.10381">arXiv:2212.10381</a> <span> [<a href="https://arxiv.org/pdf/2212.10381">pdf</a>, <a href="https://arxiv.org/format/2212.10381">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dua%2C+D">Dheeru Dua</a>, <a href="/search/cs?searchtype=author&query=Strubell%2C+E">Emma Strubell</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+S">Sameer Singh</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.10381v1-abstract-short" style="display: inline;"> Recent advances in open-domain question answering (ODQA) have demonstrated impressive accuracy on standard Wikipedia style benchmarks. However, it is less clear how robust these models are and how well they perform when applied to real-world applications in drastically different domains. While there has been some work investigating how well ODQA models perform when tested for out-of-domain (OOD) g… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.10381v1-abstract-full').style.display = 'inline'; document.getElementById('2212.10381v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.10381v1-abstract-full" style="display: none;"> Recent advances in open-domain question answering (ODQA) have demonstrated impressive accuracy on standard Wikipedia style benchmarks. However, it is less clear how robust these models are and how well they perform when applied to real-world applications in drastically different domains. While there has been some work investigating how well ODQA models perform when tested for out-of-domain (OOD) generalization, these studies have been conducted only under conservative shifts in data distribution and typically focus on a single component (ie. retrieval) rather than an end-to-end system. In response, we propose a more realistic and challenging domain shift evaluation setting and, through extensive experiments, study end-to-end model performance. We find that not only do models fail to generalize, but high retrieval scores often still yield poor answer prediction accuracy. We then categorize different types of shifts and propose techniques that, when presented with a new dataset, predict if intervention methods are likely to be successful. Finally, using insights from this analysis, we propose and evaluate several intervention methods which improve end-to-end answer F1 score by up to 24 points. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.10381v1-abstract-full').style.display = 'none'; document.getElementById('2212.10381v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.08037">arXiv:2212.08037</a> <span> [<a href="https://arxiv.org/pdf/2212.08037">pdf</a>, <a href="https://arxiv.org/format/2212.08037">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bohnet%2C+B">Bernd Bohnet</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+V+Q">Vinh Q. Tran</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=Aharoni%2C+R">Roee Aharoni</a>, <a href="/search/cs?searchtype=author&query=Andor%2C+D">Daniel Andor</a>, <a href="/search/cs?searchtype=author&query=Soares%2C+L+B">Livio Baldini Soares</a>, <a href="/search/cs?searchtype=author&query=Ciaramita%2C+M">Massimiliano Ciaramita</a>, <a href="/search/cs?searchtype=author&query=Eisenstein%2C+J">Jacob Eisenstein</a>, <a href="/search/cs?searchtype=author&query=Ganchev%2C+K">Kuzman Ganchev</a>, <a href="/search/cs?searchtype=author&query=Herzig%2C+J">Jonathan Herzig</a>, <a href="/search/cs?searchtype=author&query=Hui%2C+K">Kai Hui</a>, <a href="/search/cs?searchtype=author&query=Kwiatkowski%2C+T">Tom Kwiatkowski</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+J">Ji Ma</a>, <a href="/search/cs?searchtype=author&query=Ni%2C+J">Jianmo Ni</a>, <a href="/search/cs?searchtype=author&query=Saralegui%2C+L+S">Lierni Sestorain Saralegui</a>, <a href="/search/cs?searchtype=author&query=Schuster%2C+T">Tal Schuster</a>, <a href="/search/cs?searchtype=author&query=Cohen%2C+W+W">William W. Cohen</a>, <a href="/search/cs?searchtype=author&query=Collins%2C+M">Michael Collins</a>, <a href="/search/cs?searchtype=author&query=Das%2C+D">Dipanjan Das</a>, <a href="/search/cs?searchtype=author&query=Metzler%2C+D">Donald Metzler</a>, <a href="/search/cs?searchtype=author&query=Petrov%2C+S">Slav Petrov</a>, <a href="/search/cs?searchtype=author&query=Webster%2C+K">Kellie Webster</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.08037v2-abstract-short" style="display: inline;"> Large language models (LLMs) have shown impressive results while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios. We believe the ability of an LLM to attribute the text that it generates is likely to be crucial in this setting. We formulate and study Attributed QA as a key first step in the development of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.08037v2-abstract-full').style.display = 'inline'; document.getElementById('2212.08037v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.08037v2-abstract-full" style="display: none;"> Large language models (LLMs) have shown impressive results while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios. We believe the ability of an LLM to attribute the text that it generates is likely to be crucial in this setting. We formulate and study Attributed QA as a key first step in the development of attributed LLMs. We propose a reproducible evaluation framework for the task and benchmark a broad set of architectures. We take human annotations as a gold standard and show that a correlated automatic metric is suitable for development. Our experimental work gives concrete answers to two key questions (How to measure attribution?, and How well do current state-of-the-art methods perform on attribution?), and give some hints as to how to address a third (How to build LLMs with attribution?). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.08037v2-abstract-full').style.display = 'none'; document.getElementById('2212.08037v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.02928">arXiv:2210.02928</a> <span> [<a href="https://arxiv.org/pdf/2210.02928">pdf</a>, <a href="https://arxiv.org/format/2210.02928">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wenhu Chen</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+H">Hexiang Hu</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+X">Xi Chen</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=Cohen%2C+W+W">William W. Cohen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.02928v2-abstract-short" style="display: inline;"> While language Models store a massive amount of world knowledge implicitly in their parameters, even very large models often fail to encode information about rare entities and events, while incurring huge computational costs. Recently, retrieval-augmented models, such as REALM, RAG, and RETRO, have incorporated world knowledge into language generation by leveraging an external non-parametric index… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.02928v2-abstract-full').style.display = 'inline'; document.getElementById('2210.02928v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.02928v2-abstract-full" style="display: none;"> While language Models store a massive amount of world knowledge implicitly in their parameters, even very large models often fail to encode information about rare entities and events, while incurring huge computational costs. Recently, retrieval-augmented models, such as REALM, RAG, and RETRO, have incorporated world knowledge into language generation by leveraging an external non-parametric index and have demonstrated impressive performance with constrained model sizes. However, these methods are restricted to retrieving only textual knowledge, neglecting the ubiquitous amount of knowledge in other modalities like images -- much of which contains information not covered by any text. To address this limitation, we propose the first Multimodal Retrieval-Augmented Transformer (MuRAG), which accesses an external non-parametric multimodal memory to augment language generation. MuRAG is pre-trained with a mixture of large-scale image-text and text-only corpora using a joint contrastive and generative loss. We perform experiments on two different datasets that require retrieving and reasoning over both images and text to answer a given query: WebQA, and MultimodalQA. Our results show that MuRAG achieves state-of-the-art accuracy, outperforming existing models by 10-20\% absolute on both datasets and under both distractor and full-wiki settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.02928v2-abstract-full').style.display = 'none'; document.getElementById('2210.02928v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to EMNLP 2022 main conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.00630">arXiv:2207.00630</a> <span> [<a href="https://arxiv.org/pdf/2207.00630">pdf</a>, <a href="https://arxiv.org/format/2207.00630">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> QA Is the New KR: Question-Answer Pairs as Knowledge Bases </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wenhu Chen</a>, <a href="/search/cs?searchtype=author&query=Cohen%2C+W+W">William W. Cohen</a>, <a href="/search/cs?searchtype=author&query=De+Jong%2C+M">Michiel De Jong</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+N">Nitish Gupta</a>, <a href="/search/cs?searchtype=author&query=Presta%2C+A">Alessandro Presta</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=Wieting%2C+J">John Wieting</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2207.00630v1-abstract-short" style="display: inline;"> In this position paper, we propose a new approach to generating a type of knowledge base (KB) from text, based on question generation and entity linking. We argue that the proposed type of KB has many of the key advantages of a traditional symbolic KB: in particular, it consists of small modular components, which can be combined compositionally to answer complex queries, including relational queri… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.00630v1-abstract-full').style.display = 'inline'; document.getElementById('2207.00630v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.00630v1-abstract-full" style="display: none;"> In this position paper, we propose a new approach to generating a type of knowledge base (KB) from text, based on question generation and entity linking. We argue that the proposed type of KB has many of the key advantages of a traditional symbolic KB: in particular, it consists of small modular components, which can be combined compositionally to answer complex queries, including relational queries and queries involving "multi-hop" inferences. However, unlike a traditional KB, this information store is well-aligned with common user information needs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.00630v1-abstract-full').style.display = 'none'; document.getElementById('2207.00630v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.13761">arXiv:2204.13761</a> <span> [<a href="https://arxiv.org/pdf/2204.13761">pdf</a>, <a href="https://arxiv.org/format/2204.13761">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dong%2C+Y">Yue Dong</a>, <a href="/search/cs?searchtype=author&query=Wieting%2C+J">John Wieting</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2204.13761v1-abstract-short" style="display: inline;"> Despite recent advances in abstractive summarization, current summarization systems still suffer from content hallucinations where models generate text that is either irrelevant or contradictory to the source document. However, prior work has been predicated on the assumption that any generated facts not appearing explicitly in the source are undesired hallucinations. Methods have been proposed to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.13761v1-abstract-full').style.display = 'inline'; document.getElementById('2204.13761v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.13761v1-abstract-full" style="display: none;"> Despite recent advances in abstractive summarization, current summarization systems still suffer from content hallucinations where models generate text that is either irrelevant or contradictory to the source document. However, prior work has been predicated on the assumption that any generated facts not appearing explicitly in the source are undesired hallucinations. Methods have been proposed to address this scenario by ultimately improving `faithfulness' to the source document, but in reality, there is a large portion of entities in the gold reference targets that are not directly in the source. In this work, we show that these entities are not aberrations, but they instead require utilizing external world knowledge to infer reasoning paths from entities in the source. We show that by utilizing an external knowledge base, we can improve the faithfulness of summaries without simply making them more extractive, and additionally, we show that external knowledge bases linked from the source can benefit the factuality of generated summaries. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.13761v1-abstract-full').style.display = 'none'; document.getElementById('2204.13761v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.04581">arXiv:2204.04581</a> <span> [<a href="https://arxiv.org/pdf/2204.04581">pdf</a>, <a href="https://arxiv.org/format/2204.04581">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wenhu Chen</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=de+Jong%2C+M">Michiel de Jong</a>, <a href="/search/cs?searchtype=author&query=Wieting%2C+J">John Wieting</a>, <a href="/search/cs?searchtype=author&query=Cohen%2C+W">William Cohen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2204.04581v3-abstract-short" style="display: inline;"> Retrieval augmented language models have recently become the standard for knowledge intensive tasks. Rather than relying purely on latent semantics within the parameters of large neural models, these methods enlist a semi-parametric memory to encode an index of knowledge for the model to retrieve over. Most prior work has employed text passages as the unit of knowledge, which has high coverage at… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.04581v3-abstract-full').style.display = 'inline'; document.getElementById('2204.04581v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.04581v3-abstract-full" style="display: none;"> Retrieval augmented language models have recently become the standard for knowledge intensive tasks. Rather than relying purely on latent semantics within the parameters of large neural models, these methods enlist a semi-parametric memory to encode an index of knowledge for the model to retrieve over. Most prior work has employed text passages as the unit of knowledge, which has high coverage at the cost of interpretability, controllability, and efficiency. The opposite properties arise in other methods which have instead relied on knowledge base (KB) facts. At the same time, more recent work has demonstrated the effectiveness of storing and retrieving from an index of Q-A pairs derived from text \citep{lewis2021paq}. This approach yields a high coverage knowledge representation that maintains KB-like properties due to its representations being more atomic units of information. In this work we push this line of research further by proposing a question-answer augmented encoder-decoder model and accompanying pretraining strategy. This yields an end-to-end system that not only outperforms prior QA retrieval methods on single-hop QA tasks but also enables compositional reasoning, as demonstrated by strong performance on two multi-hop QA datasets. Together, these methods improve the ability to interpret and control the model while narrowing the performance gap with passage retrieval systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.04581v3-abstract-full').style.display = 'none'; document.getElementById('2204.04581v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by EACL 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.14364">arXiv:2109.14364</a> <span> [<a href="https://arxiv.org/pdf/2109.14364">pdf</a>, <a href="https://arxiv.org/format/2109.14364">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Multilingual Fact Linking </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kolluru%2C+K">Keshav Kolluru</a>, <a href="/search/cs?searchtype=author&query=Rezk%2C+M">Martin Rezk</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=Cohen%2C+W+W">William W. Cohen</a>, <a href="/search/cs?searchtype=author&query=Talukdar%2C+P">Partha Talukdar</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2109.14364v2-abstract-short" style="display: inline;"> Knowledge-intensive NLP tasks can benefit from linking natural language text with facts from a Knowledge Graph (KG). Although facts themselves are language-agnostic, the fact labels (i.e., language-specific representation of the fact) in the KG are often present only in a few languages. This makes it challenging to link KG facts to sentences in languages other than the limited set of languages. To… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.14364v2-abstract-full').style.display = 'inline'; document.getElementById('2109.14364v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.14364v2-abstract-full" style="display: none;"> Knowledge-intensive NLP tasks can benefit from linking natural language text with facts from a Knowledge Graph (KG). Although facts themselves are language-agnostic, the fact labels (i.e., language-specific representation of the fact) in the KG are often present only in a few languages. This makes it challenging to link KG facts to sentences in languages other than the limited set of languages. To address this problem, we introduce the task of Multilingual Fact Linking (MFL) where the goal is to link fact expressed in a sentence to corresponding fact in the KG, even when the fact label in the KG is not available in the language of the sentence. To facilitate research in this area, we present a new evaluation dataset, IndicLink. This dataset contains 11,293 linked WikiData facts and 6,429 sentences spanning English and six Indian languages. We propose a Retrieval+Generation model, ReFCoG, that can scale to millions of KG facts by combining Dual Encoder based retrieval with a Seq2Seq based generation model which is constrained to output only valid KG facts. ReFCoG outperforms standard Retrieval+Re-ranking models by 10.7 pts in Precision@1. In spite of this gain, the model achieves an overall score of 52.1, showing ample scope for improvement in the task.ReFCoG code and IndicLink data are available at https://github.com/SaiKeshav/mfl <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.14364v2-abstract-full').style.display = 'none'; document.getElementById('2109.14364v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">AKBC 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.07043">arXiv:2102.07043</a> <span> [<a href="https://arxiv.org/pdf/2102.07043">pdf</a>, <a href="https://arxiv.org/format/2102.07043">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Reasoning Over Virtual Knowledge Bases With Open Predicate Relations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sun%2C+H">Haitian Sun</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=Dhingra%2C+B">Bhuwan Dhingra</a>, <a href="/search/cs?searchtype=author&query=Salakhutdinov%2C+R">Ruslan Salakhutdinov</a>, <a href="/search/cs?searchtype=author&query=Cohen%2C+W+W">William W. Cohen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.07043v2-abstract-short" style="display: inline;"> We present the Open Predicate Query Language (OPQL); a method for constructing a virtual KB (VKB) trained entirely from text. Large Knowledge Bases (KBs) are indispensable for a wide-range of industry applications such as question answering and recommendation. Typically, KBs encode world knowledge in a structured, readily accessible form derived from laborious human annotation efforts. Unfortunate… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.07043v2-abstract-full').style.display = 'inline'; document.getElementById('2102.07043v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.07043v2-abstract-full" style="display: none;"> We present the Open Predicate Query Language (OPQL); a method for constructing a virtual KB (VKB) trained entirely from text. Large Knowledge Bases (KBs) are indispensable for a wide-range of industry applications such as question answering and recommendation. Typically, KBs encode world knowledge in a structured, readily accessible form derived from laborious human annotation efforts. Unfortunately, while they are extremely high precision, KBs are inevitably highly incomplete and automated methods for enriching them are far too inaccurate. Instead, OPQL constructs a VKB by encoding and indexing a set of relation mentions in a way that naturally enables reasoning and can be trained without any structured supervision. We demonstrate that OPQL outperforms prior VKB methods on two different KB reasoning tasks and, additionally, can be used as an external memory integrated into a language model (OPQL-LM) leading to improvements on two open-domain question answering tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.07043v2-abstract-full').style.display = 'none'; document.getElementById('2102.07043v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at the 38th International Conference on Machine Learning, PMLR 139, 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.00849">arXiv:2007.00849</a> <span> [<a href="https://arxiv.org/pdf/2007.00849">pdf</a>, <a href="https://arxiv.org/format/2007.00849">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+H">Haitian Sun</a>, <a href="/search/cs?searchtype=author&query=Soares%2C+L+B">Livio Baldini Soares</a>, <a href="/search/cs?searchtype=author&query=Cohen%2C+W+W">William W. Cohen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.00849v1-abstract-short" style="display: inline;"> Massive language models are the core of modern NLP modeling and have been shown to encode impressive amounts of commonsense and factual information. However, that knowledge exists only within the latent parameters of the model, inaccessible to inspection and interpretation, and even worse, factual information memorized from the training corpora is likely to become stale as the world changes. Knowl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.00849v1-abstract-full').style.display = 'inline'; document.getElementById('2007.00849v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.00849v1-abstract-full" style="display: none;"> Massive language models are the core of modern NLP modeling and have been shown to encode impressive amounts of commonsense and factual information. However, that knowledge exists only within the latent parameters of the model, inaccessible to inspection and interpretation, and even worse, factual information memorized from the training corpora is likely to become stale as the world changes. Knowledge stored as parameters will also inevitably exhibit all of the biases inherent in the source materials. To address these problems, we develop a neural language model that includes an explicit interface between symbolically interpretable factual information and subsymbolic neural knowledge. We show that this model dramatically improves performance on two knowledge-intensive question-answering tasks. More interestingly, the model can be updated without re-training by manipulating its symbolic representations. In particular this model allows us to add new facts and overwrite existing ones in ways that are not possible for earlier models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.00849v1-abstract-full').style.display = 'none'; document.getElementById('2007.00849v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1912.01070">arXiv:1912.01070</a> <span> [<a href="https://arxiv.org/pdf/1912.01070">pdf</a>, <a href="https://arxiv.org/format/1912.01070">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bansal%2C+T">Trapit Bansal</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=Choudhary%2C+N">Neha Choudhary</a>, <a href="/search/cs?searchtype=author&query=McCallum%2C+A">Andrew McCallum</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1912.01070v1-abstract-short" style="display: inline;"> Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonical concept, and discerning their relationships. These tasks are nearly always viewed as separate components within a pipeline, each requiring a distinct model and training data. While relation extraction can often be tra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.01070v1-abstract-full').style.display = 'inline'; document.getElementById('1912.01070v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1912.01070v1-abstract-full" style="display: none;"> Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonical concept, and discerning their relationships. These tasks are nearly always viewed as separate components within a pipeline, each requiring a distinct model and training data. While relation extraction can often be trained with readily available weak or distant supervision, entity linkers typically require expensive mention-level supervision -- which is not available in many domains. Instead, we propose a model which is trained to simultaneously produce entity linking and relation decisions while requiring no mention-level annotations. This approach avoids cascading errors that arise from pipelined methods and more accurately predicts entity relationships from text. We show that our model outperforms a state-of-the art entity linking and relation extraction pipeline on two biomedical datasets and can drastically improve the overall recall of the system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.01070v1-abstract-full').style.display = 'none'; document.getElementById('1912.01070v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 December, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in AAAI 2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.02142">arXiv:1904.02142</a> <span> [<a href="https://arxiv.org/pdf/1904.02142">pdf</a>, <a href="https://arxiv.org/format/1904.02142">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Drozdov%2C+A">Andrew Drozdov</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Pat Verga</a>, <a href="/search/cs?searchtype=author&query=Yadav%2C+M">Mohit Yadav</a>, <a href="/search/cs?searchtype=author&query=Iyyer%2C+M">Mohit Iyyer</a>, <a href="/search/cs?searchtype=author&query=McCallum%2C+A">Andrew McCallum</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.02142v2-abstract-short" style="display: inline;"> We introduce deep inside-outside recursive autoencoders (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Our approach predicts each word in an input sentence conditioned on the rest of the sentence and uses inside-outside dynamic programming to consider all possible binary trees over the sentence. At te… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.02142v2-abstract-full').style.display = 'inline'; document.getElementById('1904.02142v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.02142v2-abstract-full" style="display: none;"> We introduce deep inside-outside recursive autoencoders (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Our approach predicts each word in an input sentence conditioned on the rest of the sentence and uses inside-outside dynamic programming to consider all possible binary trees over the sentence. At test time the CKY algorithm extracts the highest scoring parse. DIORA achieves a new state-of-the-art F1 in unsupervised binary constituency parsing (unlabeled) in two benchmark datasets, WSJ and MultiNLI. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.02142v2-abstract-full').style.display = 'none'; document.getElementById('1904.02142v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 8 figures, 8 tables. NAACL 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1804.08199">arXiv:1804.08199</a> <span> [<a href="https://arxiv.org/pdf/1804.08199">pdf</a>, <a href="https://arxiv.org/format/1804.08199">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Linguistically-Informed Self-Attention for Semantic Role Labeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Strubell%2C+E">Emma Strubell</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Patrick Verga</a>, <a href="/search/cs?searchtype=author&query=Andor%2C+D">Daniel Andor</a>, <a href="/search/cs?searchtype=author&query=Weiss%2C+D">David Weiss</a>, <a href="/search/cs?searchtype=author&query=McCallum%2C+A">Andrew McCallum</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1804.08199v3-abstract-short" style="display: inline;"> Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a neural network model that combin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.08199v3-abstract-full').style.display = 'inline'; document.getElementById('1804.08199v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1804.08199v3-abstract-full" style="display: none;"> Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL. Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates. Syntax is incorporated by training one attention head to attend to syntactic parents for each token. Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model. In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in error. On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1. LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.08199v3-abstract-full').style.display = 'none'; document.getElementById('1804.08199v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">In Conference on Empirical Methods in Natural Language Processing (EMNLP). Brussels, Belgium. October 2018</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1802.10569">arXiv:1802.10569</a> <span> [<a href="https://arxiv.org/pdf/1802.10569">pdf</a>, <a href="https://arxiv.org/format/1802.10569">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Verga%2C+P">Patrick Verga</a>, <a href="/search/cs?searchtype=author&query=Strubell%2C+E">Emma Strubell</a>, <a href="/search/cs?searchtype=author&query=McCallum%2C+A">Andrew McCallum</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1802.10569v1-abstract-short" style="display: inline;"> Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. This approach often does not consider interactions across mentions, requires redundant computation for each mention pair, and ignores relationships expressed across sentence boundaries. These problems are exacerbated by the document- (rather than… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.10569v1-abstract-full').style.display = 'inline'; document.getElementById('1802.10569v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1802.10569v1-abstract-full" style="display: none;"> Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. This approach often does not consider interactions across mentions, requires redundant computation for each mention pair, and ignores relationships expressed across sentence boundaries. These problems are exacerbated by the document- (rather than sentence-) level annotation common in biological text. In response, we propose a model which simultaneously predicts relationships between all mention pairs in a document. We form pairwise predictions over entire paper abstracts using an efficient self-attention encoder. All-pairs mention scores allow us to perform multi-instance learning by aggregating over mentions to form entity pair representations. We further adapt to settings without mention-level annotation by jointly training to predict named entities and adding a corpus of weakly labeled data. In experiments on two Biocreative benchmark datasets, we achieve state of the art performance on the Biocreative V Chemical Disease Relation dataset for models without external KB resources. We also introduce a new dataset an order of magnitude larger than existing human-annotated biological information extraction datasets and more accurate than distantly supervised alternatives. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1802.10569v1-abstract-full').style.display = 'none'; document.getElementById('1802.10569v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 February, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">NAACL 2018</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1711.05795">arXiv:1711.05795</a> <span> [<a href="https://arxiv.org/pdf/1711.05795">pdf</a>, <a href="https://arxiv.org/format/1711.05795">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Finer Grained Entity Typing with TypeNet </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Murty%2C+S">Shikhar Murty</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Patrick Verga</a>, <a href="/search/cs?searchtype=author&query=Vilnis%2C+L">Luke Vilnis</a>, <a href="/search/cs?searchtype=author&query=McCallum%2C+A">Andrew McCallum</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1711.05795v1-abstract-short" style="display: inline;"> We consider the challenging problem of entity typing over an extremely fine grained set of types, wherein a single mention or entity can have many simultaneous and often hierarchically-structured types. Despite the importance of the problem, there is a relative lack of resources in the form of fine-grained, deep type hierarchies aligned to existing knowledge bases. In response, we introduce TypeNe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.05795v1-abstract-full').style.display = 'inline'; document.getElementById('1711.05795v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.05795v1-abstract-full" style="display: none;"> We consider the challenging problem of entity typing over an extremely fine grained set of types, wherein a single mention or entity can have many simultaneous and often hierarchically-structured types. Despite the importance of the problem, there is a relative lack of resources in the form of fine-grained, deep type hierarchies aligned to existing knowledge bases. In response, we introduce TypeNet, a dataset of entity types consisting of over 1941 types organized in a hierarchy, obtained by manually annotating a mapping from 1081 Freebase types to WordNet. We also experiment with several models comparable to state-of-the-art systems and explore techniques to incorporate a structure loss on the hierarchy with the standard mention typing loss, as a first step towards future research on this dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.05795v1-abstract-full').style.display = 'none'; document.getElementById('1711.05795v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at 6th Workshop on Automated Knowledge Base Construction (AKBC) at NIPS 2017</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1710.08312">arXiv:1710.08312</a> <span> [<a href="https://arxiv.org/pdf/1710.08312">pdf</a>, <a href="https://arxiv.org/format/1710.08312">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Attending to All Mention Pairs for Full Abstract Biological Relation Extraction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Verga%2C+P">Patrick Verga</a>, <a href="/search/cs?searchtype=author&query=Strubell%2C+E">Emma Strubell</a>, <a href="/search/cs?searchtype=author&query=Shai%2C+O">Ofer Shai</a>, <a href="/search/cs?searchtype=author&query=McCallum%2C+A">Andrew McCallum</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1710.08312v2-abstract-short" style="display: inline;"> Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. However, many relation types, particularly in biomedical text, are expressed across sentences or require a large context to disambiguate. We propose a model to consider all mention and entity pairs simultaneously in order to make a prediction. We… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.08312v2-abstract-full').style.display = 'inline'; document.getElementById('1710.08312v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1710.08312v2-abstract-full" style="display: none;"> Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. However, many relation types, particularly in biomedical text, are expressed across sentences or require a large context to disambiguate. We propose a model to consider all mention and entity pairs simultaneously in order to make a prediction. We encode full paper abstracts using an efficient self-attention encoder and form pairwise predictions between all mentions with a bi-affine operation. An entity-pair wise pooling aggregates mention pair scores to make a final prediction while alleviating training noise by performing within document multi-instance learning. We improve our model's performance by jointly training the model to predict named entities and adding an additional corpus of weakly labeled data. We demonstrate our model's effectiveness by achieving the state of the art on the Biocreative V Chemical Disease Relation dataset for models without KB resources, outperforming ensembles of models which use hand-crafted features and additional linguistic resources. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.08312v2-abstract-full').style.display = 'none'; document.getElementById('1710.08312v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 October, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6th Workshop on Automated Knowledge Base Construction (AKBC)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1702.02098">arXiv:1702.02098</a> <span> [<a href="https://arxiv.org/pdf/1702.02098">pdf</a>, <a href="https://arxiv.org/format/1702.02098">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Fast and Accurate Entity Recognition with Iterated Dilated Convolutions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Strubell%2C+E">Emma Strubell</a>, <a href="/search/cs?searchtype=author&query=Verga%2C+P">Patrick Verga</a>, <a href="/search/cs?searchtype=author&query=Belanger%2C+D">David Belanger</a>, <a href="/search/cs?searchtype=author&query=McCallum%2C+A">Andrew McCallum</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1702.02098v3-abstract-short" style="display: inline;"> Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per-token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF).… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1702.02098v3-abstract-full').style.display = 'inline'; document.getElementById('1702.02098v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1702.02098v3-abstract-full" style="display: none;"> Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per-token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. Unlike LSTMs whose sequential processing on sentences of length N requires O(N) time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions to run in parallel across entire documents. We describe a distinct combination of network structure, parameter sharing and training procedures that enable dramatic 14-20x test-time speedups while retaining accuracy comparable to the Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire document are even more accurate while maintaining 8x faster test time speeds. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1702.02098v3-abstract-full').style.display = 'none'; document.getElementById('1702.02098v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 July, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 February, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">In Conference on Empirical Methods in Natural Language Processing (EMNLP). Copenhagen, Denmark. September 2017</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1606.05804">arXiv:1606.05804</a> <span> [<a href="https://arxiv.org/pdf/1606.05804">pdf</a>, <a href="https://arxiv.org/format/1606.05804">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Verga%2C+P">Patrick Verga</a>, <a href="/search/cs?searchtype=author&query=Neelakantan%2C+A">Arvind Neelakantan</a>, <a href="/search/cs?searchtype=author&query=McCallum%2C+A">Andrew McCallum</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1606.05804v2-abstract-short" style="display: inline;"> Universal schema predicts the types of entities and relations in a knowledge base (KB) by jointly embedding the union of all available schema types---not only types from multiple structured databases (such as Freebase or Wikipedia infoboxes), but also types expressed as textual patterns from raw text. This prediction is typically modeled as a matrix completion problem, with one type per column, an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.05804v2-abstract-full').style.display = 'inline'; document.getElementById('1606.05804v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1606.05804v2-abstract-full" style="display: none;"> Universal schema predicts the types of entities and relations in a knowledge base (KB) by jointly embedding the union of all available schema types---not only types from multiple structured databases (such as Freebase or Wikipedia infoboxes), but also types expressed as textual patterns from raw text. This prediction is typically modeled as a matrix completion problem, with one type per column, and either one or two entities per row (in the case of entity types or binary relation types, respectively). Factorizing this sparsely observed matrix yields a learned vector embedding for each row and each column. In this paper we explore the problem of making predictions for entities or entity-pairs unseen at training time (and hence without a pre-learned row embedding). We propose an approach having no per-row parameters at all; rather we produce a row vector on the fly using a learned aggregation function of the vectors of the observed columns for that row. We experiment with various aggregation functions, including neural network attention models. Our approach can be understood as a natural language database, in that questions about KB entities are answered by attending to textual or database evidence. In experiments predicting both relations and entity types, we demonstrate that despite having an order of magnitude fewer parameters than traditional universal schema, we can match the accuracy of the traditional model, and more importantly, we can now make predictions about unseen rows with nearly the same accuracy as rows available at training time. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.05804v2-abstract-full').style.display = 'none'; document.getElementById('1606.05804v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 January, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 June, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">EACL 2017. arXiv admin note: text overlap with arXiv:1604.06361</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1604.06361">arXiv:1604.06361</a> <span> [<a href="https://arxiv.org/pdf/1604.06361">pdf</a>, <a href="https://arxiv.org/format/1604.06361">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Row-less Universal Schema </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Verga%2C+P">Patrick Verga</a>, <a href="/search/cs?searchtype=author&query=McCallum%2C+A">Andrew McCallum</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1604.06361v1-abstract-short" style="display: inline;"> Universal schema jointly embeds knowledge bases and textual patterns to reason about entities and relations for automatic knowledge base construction and information extraction. In the past, entity pairs and relations were represented as learned vectors with compatibility determined by a scoring function, limiting generalization to unseen text patterns and entities. Recently, 'column-less' version… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1604.06361v1-abstract-full').style.display = 'inline'; document.getElementById('1604.06361v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1604.06361v1-abstract-full" style="display: none;"> Universal schema jointly embeds knowledge bases and textual patterns to reason about entities and relations for automatic knowledge base construction and information extraction. In the past, entity pairs and relations were represented as learned vectors with compatibility determined by a scoring function, limiting generalization to unseen text patterns and entities. Recently, 'column-less' versions of Universal Schema have used compositional pattern encoders to generalize to all text patterns. In this work we take the next step and propose a 'row-less' model of universal schema, removing explicit entity pair representations. Instead of learning vector representations for each entity pair in our training set, we treat an entity pair as a function of its relation types. In experimental results on the FB15k-237 benchmark we demonstrate that we can match the performance of a comparable model with explicit entity pair representations using a model of attention over relation types. We further demonstrate that the model per- forms with nearly the same accuracy on entity pairs never seen during training. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1604.06361v1-abstract-full').style.display = 'none'; document.getElementById('1604.06361v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 April, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">AKBC 2016 Workshop</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1511.06396">arXiv:1511.06396</a> <span> [<a href="https://arxiv.org/pdf/1511.06396">pdf</a>, <a href="https://arxiv.org/format/1511.06396">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Multilingual Relation Extraction using Compositional Universal Schema </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Verga%2C+P">Patrick Verga</a>, <a href="/search/cs?searchtype=author&query=Belanger%2C+D">David Belanger</a>, <a href="/search/cs?searchtype=author&query=Strubell%2C+E">Emma Strubell</a>, <a href="/search/cs?searchtype=author&query=Roth%2C+B">Benjamin Roth</a>, <a href="/search/cs?searchtype=author&query=McCallum%2C+A">Andrew McCallum</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1511.06396v2-abstract-short" style="display: inline;"> Universal schema builds a knowledge base (KB) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns expressing relations from raw text. In most previous applications of universal schema, each textual pattern is represented as a single embedding, preventing generalization to unseen patterns. Recent work employs a neural network to capture patte… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1511.06396v2-abstract-full').style.display = 'inline'; document.getElementById('1511.06396v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1511.06396v2-abstract-full" style="display: none;"> Universal schema builds a knowledge base (KB) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns expressing relations from raw text. In most previous applications of universal schema, each textual pattern is represented as a single embedding, preventing generalization to unseen patterns. Recent work employs a neural network to capture patterns' compositional semantics, providing generalization to all possible input text. In response, this paper introduces significant further improvements to the coverage and flexibility of universal schema relation extraction: predictions for entities unseen in training and multilingual transfer learning to domains with no annotation. We evaluate our model through extensive experiments on the English and Spanish TAC KBP benchmark, outperforming the top system from TAC 2013 slot-filling using no handwritten patterns or additional annotation. We also consider a multilingual setting in which English training data entities overlap with the seed KB, but Spanish text does not. Despite having no annotation for Spanish data, we train an accurate predictor, with additional improvements obtained by tying word embeddings across languages. Furthermore, we find that multilingual training improves English relation extraction accuracy. Our approach is thus suited to broad-coverage automated knowledge base construction in a variety of languages and domains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1511.06396v2-abstract-full').style.display = 'none'; document.getElementById('1511.06396v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 March, 2016; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 November, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to NAACL 2016</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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