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<!DOCTYPE html> <html > <head> <meta charset="utf-8"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <meta content="width=device-width, initial-scale=1" name="viewport"> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs"> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="GM91khOT-QTng5AYYSfEaxeR8UKsDm-the7uFQdLO5S_E5ybkLvZl83w3PPWxiLVsJdfFeeQPjSqp_adKbr8xQ" /> <meta name="citation_title" content="Learning Cross-Lingual Word Embeddings with Universal Concepts" /> <meta name="citation_publication_date" content="2019/01/01" /> <meta name="citation_journal_title" content="International Journal on Web Service Computing (IJWSC)" /> <meta name="citation_author" content="Pezhman Sheinidashtegol" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/58500179/Learning_Cross_Lingual_Word_Embeddings_with_Universal_Concepts" /> <meta name="twitter:title" content="Learning Cross-Lingual Word Embeddings with Universal Concepts" /> <meta name="twitter:description" content="Recent advances in generating monolingual word embeddings based on word co-occurrence for universal languages inspired new efforts to extend the model to support diversified languages. State-of-the-art methods for learning cross-lingual word" /> <meta name="twitter:image" content="http://a.academia-assets.com/images/twitter-card.jpeg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/58500179/Learning_Cross_Lingual_Word_Embeddings_with_Universal_Concepts" /> <meta property="og:title" content="Learning Cross-Lingual Word Embeddings with Universal Concepts" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="Recent advances in generating monolingual word embeddings based on word co-occurrence for universal languages inspired new efforts to extend the model to support diversified languages. State-of-the-art methods for learning cross-lingual word" /> <meta property="article:author" content="https://independent.academia.edu/PezhmanSheinidashtegol" /> <meta name="description" content="Recent advances in generating monolingual word embeddings based on word co-occurrence for universal languages inspired new efforts to extend the model to support diversified languages. State-of-the-art methods for learning cross-lingual word" /> <title>Learning Cross-Lingual Word Embeddings with Universal Concepts</title> <link rel="canonical" href="https://www.academia.edu/58500179/Learning_Cross_Lingual_Word_Embeddings_with_Universal_Concepts" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '65688b5f01769e4981f5a2be5e5aa7813b2e8d05'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1740598597000); window.Aedu.timeDifference = new Date().getTime() - 1740598597000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"Recent advances in generating monolingual word embeddings based on word co-occurrence for universal languages inspired new efforts to extend the model to support diversified languages. State-of-the-art methods for learning cross-lingual word embeddings rely on the alignment of monolingual word embedding spaces. Our goal is to implement a word co-occurrence across languages with the universal concepts\u0026amp;amp;#39; method. Such concepts are notions that are fundamental to humankind and are thus persistent across languages, e.g., a man or woman, war or peace, etc. Given bilingual lexicons, we built universal concepts as undirected graphs of connected nodes and then replaced the words belonging to the same graph with a unique graph ID. This intuitive design makes use of universal concepts in monolingual corpora which will help generate meaningful word embeddings across languages via the word co-occurrence concept. Standardized benchmarks demonstrate how this underutilized approach competes SOTA...","author":[{"@context":"https://schema.org","@type":"Person","name":"Pezhman Sheinidashtegol","url":"https://independent.academia.edu/PezhmanSheinidashtegol"}],"contributor":[],"dateCreated":"2021-10-16","dateModified":"2021-10-16","datePublished":"2019-01-01","headline":"Learning Cross-Lingual Word Embeddings with Universal Concepts","image":"https://attachments.academia-assets.com/72880713/thumbnails/1.jpg","inLanguage":"en","keywords":["Bilingual Education","Computational Linguistics \u0026 NLP","universal concepts","Word Embedding Model","Word Embedding Evaluation Tasks","Bilingual and Cross-lingual Word Embeddings"],"publication":"International Journal on Web Service Computing (IJWSC)","publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}],"thumbnailUrl":"https://attachments.academia-assets.com/72880713/thumbnails/1.jpg","url":"https://www.academia.edu/58500179/Learning_Cross_Lingual_Word_Embeddings_with_Universal_Concepts"}</script><style type="text/css">@media(max-width: 567px){:root{--token-mode: Rebrand;--dropshadow: 0 2px 4px 0 #22223340;--primary-brand: #0645b1;--error-dark: #b60000;--success-dark: #05b01c;--inactive-fill: #ebebee;--hover: #0c3b8d;--pressed: #082f75;--button-primary-fill-inactive: #ebebee;--button-primary-fill: #0645b1;--button-primary-text: #ffffff;--button-primary-fill-hover: #0c3b8d;--button-primary-fill-press: #082f75;--button-primary-icon: #ffffff;--button-primary-fill-inverse: #ffffff;--button-primary-text-inverse: #082f75;--button-primary-icon-inverse: #0645b1;--button-primary-fill-inverse-hover: 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{"work":{"id":58500179,"created_at":"2021-10-16T15:51:42.062-07:00","from_world_paper_id":179838361,"updated_at":"2021-10-16T16:13:44.396-07:00","_data":{"abstract":"Recent advances in generating monolingual word embeddings based on word co-occurrence for universal languages inspired new efforts to extend the model to support diversified languages. State-of-the-art methods for learning cross-lingual word embeddings rely on the alignment of monolingual word embedding spaces. Our goal is to implement a word co-occurrence across languages with the universal concepts\u0026#39; method. Such concepts are notions that are fundamental to humankind and are thus persistent across languages, e.g., a man or woman, war or peace, etc. Given bilingual lexicons, we built universal concepts as undirected graphs of connected nodes and then replaced the words belonging to the same graph with a unique graph ID. This intuitive design makes use of universal concepts in monolingual corpora which will help generate meaningful word embeddings across languages via the word co-occurrence concept. Standardized benchmarks demonstrate how this underutilized approach competes SOTA...","publication_date":"2019,,","publication_name":"International Journal on Web Service Computing (IJWSC)"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Learning Cross-Lingual Word Embeddings with Universal Concepts","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [77264013]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":72880713,"attachmentType":"pdf"}"><img alt="First page of “Learning Cross-Lingual Word Embeddings with Universal Concepts”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/72880713/mini_magick20211016-13528-ceihil.png?1634424735" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Learning Cross-Lingual Word Embeddings with Universal Concepts</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="77264013" href="https://independent.academia.edu/PezhmanSheinidashtegol"><img alt="Profile image of Pezhman Sheinidashtegol" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Pezhman Sheinidashtegol</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2019, International Journal on Web Service Computing (IJWSC)</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">8 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 58500179; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=58500179"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Recent advances in generating monolingual word embeddings based on word co-occurrence for universal languages inspired new efforts to extend the model to support diversified languages. State-of-the-art methods for learning cross-lingual word embeddings rely on the alignment of monolingual word embedding spaces. Our goal is to implement a word co-occurrence across languages with the universal concepts&#39; method. Such concepts are notions that are fundamental to humankind and are thus persistent across languages, e.g., a man or woman, war or peace, etc. Given bilingual lexicons, we built universal concepts as undirected graphs of connected nodes and then replaced the words belonging to the same graph with a unique graph ID. This intuitive design makes use of universal concepts in monolingual corpora which will help generate meaningful word embeddings across languages via the word co-occurrence concept. Standardized benchmarks demonstrate how this underutilized approach competes SOTA...</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":72880713,"attachmentType":"pdf","workUrl":"https://www.academia.edu/58500179/Learning_Cross_Lingual_Word_Embeddings_with_Universal_Concepts"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":72880713,"attachmentType":"pdf","workUrl":"https://www.academia.edu/58500179/Learning_Cross_Lingual_Word_Embeddings_with_Universal_Concepts"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-premium-marketing"></div></div><div class="ds-signup-banner ds-signup-banner-premium-marketing"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="premium-banner-content" data-impression-entity-id="58500179" data-impression-entity-type="2" data-impression-source="premium-banner-desktop"><div class="left"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><span>Get access to the world's latest research</span></div><div class="right"><div class="card free"><div class="header">Free</div><div class="feature-list"><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Download one paper at a time</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Save papers to bookmarks</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Basic search</span></div></div><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--small ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"premium-banner-desktop-free"}">Sign up for free</button></div><div class="card premium"><div class="pill">Recommended</div><div class="header premium">Premium</div><div class="feature-list"><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Get highly curated PDF packages</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Track your impact with Mentions</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Access advanced search filters</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Support Academia’s mission</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Create your personal website</span></div></div><button class="ds2-5-button ds2-5-button--small ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"premium-banner-desktop-upgrade","submitText":"Try Premium for $1"}">Try Premium for $1</button></div></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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These knowledge resources (like WordNet, Paraphrase Database) may not exist for all languages. In this work we introduce a method to inject word embeddings of a language with knowledge resource of another language by leveraging bilingual embeddings. First we improve word embeddings of German, Italian, French and Spanish using resources of English and test them on variety of word similarity tasks. Then we demonstrate the utility of our method by creating improved embeddings for Urdu and Telugu languages using Hindi WordNet, beating the previously established baseline for Urdu.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Injecting Word Embeddings with Another Language’s Resource : An Application of Bilingual Embeddings","attachmentId":89908541,"attachmentType":"pdf","work_url":"https://www.academia.edu/85103486/Injecting_Word_Embeddings_with_Another_Language_s_Resource_An_Application_of_Bilingual_Embeddings","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/85103486/Injecting_Word_Embeddings_with_Another_Language_s_Resource_An_Application_of_Bilingual_Embeddings"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="121847689" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/121847689/Bilingual_Word_Representations_with_Monolingual_Quality_in_Mind">Bilingual Word Representations with Monolingual Quality in Mind</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="17874" href="https://stanford.academia.edu/ChristopherManning">Christopher D Manning</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, 2015</p><p class="ds-related-work--abstract ds2-5-body-sm">Recent work in learning bilingual representations tend to tailor towards achieving good performance on bilingual tasks, most often the crosslingual document classification (CLDC) evaluation, but to the detriment of preserving clustering structures of word representations monolingually. In this work, we propose a joint model to learn word representations from scratch that utilizes both the context coocurrence information through the monolingual component and the meaning equivalent signals from the bilingual constraint. Specifically, we extend the recently popular skipgram model to learn high quality bilingual representations efficiently. Our learned embeddings achieve a new state-of-the-art accuracy of 80.3 for the German to English CLDC task and a highly competitive performance of 90.7 for the other classification direction. At the same time, our models outperform best embeddings from past bilingual representation work by a large margin in the monolingual word similarity evaluation. 1</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Bilingual Word Representations with Monolingual Quality in Mind","attachmentId":116632763,"attachmentType":"pdf","work_url":"https://www.academia.edu/121847689/Bilingual_Word_Representations_with_Monolingual_Quality_in_Mind","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/121847689/Bilingual_Word_Representations_with_Monolingual_Quality_in_Mind"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="116716647" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/116716647/Leveraging_Vector_Space_Similarity_for_Learning_Cross_Lingual_Word_Embeddings_A_Systematic_Review">Leveraging Vector Space Similarity for Learning Cross-Lingual Word Embeddings: A Systematic Review</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="862273" href="https://uc.academia.edu/AncaRalescu">Anca Ralescu</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Digital, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">This article presents a systematic literature review on quantifying the proximity between independently trained monolingual word embedding spaces. A search was carried out in the broader context of inducing bilingual lexicons from cross-lingual word embeddings, especially for low-resource languages. The returned articles were then classified. Cross-lingual word embeddings have drawn the attention of researchers in the field of natural language processing (NLP). Although existing methods have yielded satisfactory results for resource-rich languages and languages related to them, some researchers have pointed out that the same is not true for low-resource and distant languages. In this paper, we report the research on methods proposed to provide better representation for low-resource and distant languages in the cross-lingual word embedding space.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Leveraging Vector Space Similarity for Learning Cross-Lingual Word Embeddings: A Systematic Review","attachmentId":112768868,"attachmentType":"pdf","work_url":"https://www.academia.edu/116716647/Leveraging_Vector_Space_Similarity_for_Learning_Cross_Lingual_Word_Embeddings_A_Systematic_Review","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/116716647/Leveraging_Vector_Space_Similarity_for_Learning_Cross_Lingual_Word_Embeddings_A_Systematic_Review"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="83618399" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/83618399/Discovering_Bilingual_Lexicons_in_Polyglot_Word_Embeddings">Discovering Bilingual Lexicons in Polyglot Word Embeddings</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="440647" href="https://cmu.academia.edu/AshiqurKhudaBukhsh">Ashiqur KhudaBukhsh</a></div><p class="ds-related-work--metadata ds2-5-body-xs">ArXiv, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Bilingual lexicons and phrase tables are critical resources for modern Machine Translation systems. Although recent results show that without any seed lexicon or parallel data, highly accurate bilingual lexicons can be learned using unsupervised methods, such methods rely on the existence of large, clean monolingual corpora. In this work, we utilize a single Skip-gram model trained on a multilingual corpus yielding polyglot word embeddings, and present a novel finding that a surprisingly simple constrained nearest-neighbor sampling technique in this embedding space can retrieve bilingual lexicons, even in harsh social media data sets predominantly written in English and Romanized Hindi and often exhibiting code switching. Our method does not require monolingual corpora, seed lexicons, or any other such resources. Additionally, across three European language pairs, we observe that polyglot word embeddings indeed learn a rich semantic representation of words and substantial bilingual ...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Discovering Bilingual Lexicons in Polyglot Word Embeddings","attachmentId":88904166,"attachmentType":"pdf","work_url":"https://www.academia.edu/83618399/Discovering_Bilingual_Lexicons_in_Polyglot_Word_Embeddings","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/83618399/Discovering_Bilingual_Lexicons_in_Polyglot_Word_Embeddings"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="79834362" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/79834362/Cross_Lingual_Contextual_Word_Embeddings_Mapping_With_Multi_Sense_Words_In_Mind">Cross-Lingual Contextual Word Embeddings Mapping With Multi-Sense Words In Mind</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="118014443" href="https://independent.academia.edu/JunZHU31">Jun ZHU</a></div><p class="ds-related-work--metadata ds2-5-body-xs">ArXiv, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Recent work in cross-lingual contextual word embedding learning cannot handle multi-sense words well. In this work, we explore the characteristics of contextual word embeddings and show the link between contextual word embeddings and word senses. We propose two improving solutions by considering contextual multi-sense word embeddings as noise (removal) and by generating cluster level average anchor embeddings for contextual multi-sense word embeddings (replacement). Experiments show that our solutions can improve the supervised contextual word embeddings alignment for multi-sense words in a microscopic perspective without hurting the macroscopic performance on the bilingual lexicon induction task. For unsupervised alignment, our methods significantly improve the performance on the bilingual lexicon induction task for more than 10 points.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Cross-Lingual Contextual Word Embeddings Mapping With Multi-Sense Words In Mind","attachmentId":86415573,"attachmentType":"pdf","work_url":"https://www.academia.edu/79834362/Cross_Lingual_Contextual_Word_Embeddings_Mapping_With_Multi_Sense_Words_In_Mind","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/79834362/Cross_Lingual_Contextual_Word_Embeddings_Mapping_With_Multi_Sense_Words_In_Mind"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="107760041" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/107760041/Exploring_cross_lingual_word_embeddings_for_the_inference_of_bilingual_dictionaries">Exploring cross-lingual word embeddings for the inference of bilingual dictionaries</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="873339" href="https://coruna.academia.edu/MiguelAngelAlonsoPardo">Miguel Angel Alonso Pardo</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2019</p><p class="ds-related-work--abstract ds2-5-body-sm">We describe four systems to generate automatically bilingual dictionaries based on existing ones: three transitive systems differing only in the pivot language used, and a system based on a different approach which only needs monolingual corpora in both the source and target languages. All four methods make use of cross-lingual word embeddings trained on monolingual corpora, and then mapped into a shared vec- tor space. Experimental results confirm that our strategy has a good coverage and recall, achieving a performance comparable to to the best submitted systems on the TIAD 2019 gold standard set among the teams participating at the TIAD shared task.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Exploring cross-lingual word embeddings for the inference of bilingual dictionaries","attachmentId":106335260,"attachmentType":"pdf","work_url":"https://www.academia.edu/107760041/Exploring_cross_lingual_word_embeddings_for_the_inference_of_bilingual_dictionaries","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/107760041/Exploring_cross_lingual_word_embeddings_for_the_inference_of_bilingual_dictionaries"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="94168125" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/94168125/Training_vs_Post_training_Cross_lingual_Word_Embedding_Approaches_A_Comparative_Study">Training vs. Post-training Cross-lingual Word Embedding Approaches: A Comparative Study</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="3427538" href="https://ihcs.academia.edu/MasoodGhayoomi">Masood Ghayoomi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Information Science and Management, 2023</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper provides a comparative analysis of cross-lingual word embedding by studying the impact of different variables on the quality of the embedding models within the distributional semantics framework. Distributional semantics is a method for the semantic representation of words, phrases, sentences, and documents. This method aims at capturing as much information as possible from the contextual information in a vector space. The early study in this domain focused on monolingual word embedding. Further progress used cross-lingual data to capture the contextual semantic information across different languages. The main contribution of this research is to make a comparative study to find out the superior impact of the learning methods, supervised and unsupervised in training and post-training approaches in different embedding algorithms, to capture semantic properties of the words in cross-lingual embedding models to be applicable in tasks that deal with multi-languages, such as question retrieval. To this end, we study the cross-lingual embedding models created by BilBOWA, VecMap, and MUSE embedding algorithms along with the variables that impact the embedding models' quality, namely the size of the training data and the window size of the local context. In our study, we use the unsupervised monolingual Word2Vec embedding model as the baseline and evaluate the quality of embeddings on three data sets: Google analogy, mono-and cross-lingual words similar lists. We further investigated the impact of the embedding models in the question retrieval task.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Training vs. Post-training Cross-lingual Word Embedding Approaches: A Comparative Study","attachmentId":96701073,"attachmentType":"pdf","work_url":"https://www.academia.edu/94168125/Training_vs_Post_training_Cross_lingual_Word_Embedding_Approaches_A_Comparative_Study","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/94168125/Training_vs_Post_training_Cross_lingual_Word_Embedding_Approaches_A_Comparative_Study"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="66915094" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/66915094/The_Limitations_of_Cross_language_Word_Embeddings_Evaluation">The Limitations of Cross-language Word Embeddings Evaluation</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="155946849" href="https://independent.academia.edu/RSuvorov">Roman Suvorov</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">The aim of this work is to explore the possible limitations of existing methods of crosslanguage word embeddings evaluation, addressing the lack of correlation between intrinsic and extrinsic cross-language evaluation methods. To prove this hypothesis, we construct English-Russian datasets for extrinsic and intrinsic evaluation tasks and compare performances of 5 different cross-language models on them. The results say that the scores even on different intrinsic benchmarks do not correlate to each other. We can conclude that the use of human references as ground truth for cross-language word embeddings is not proper unless one does not understand how do native speakers process semantics in their cognition.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"The Limitations of Cross-language Word Embeddings Evaluation","attachmentId":77929150,"attachmentType":"pdf","work_url":"https://www.academia.edu/66915094/The_Limitations_of_Cross_language_Word_Embeddings_Evaluation","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/66915094/The_Limitations_of_Cross_language_Word_Embeddings_Evaluation"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="76289705" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/76289705/Word_embedding_based_bilingual_terminology_alignment">Word-embedding based bilingual terminology alignment</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34767207" href="https://independent.academia.edu/SenjaPollak">Senja Pollak</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021</p><p class="ds-related-work--abstract ds2-5-body-sm">The ability to accurately align concepts between languages can provide significant benefits in many practical applications. In this paper, we extend a machine learning approach using dictionary and cognate-based features with novel cross-lingual embedding features using pretrained fastText embeddings. We use the tool VecMap to align the embeddings between Slovenian and English and then for every word calculate the top 3 closest word embeddings in the opposite language based on cosine distance. These alignments are then used as features for the machine learning algorithm. With one configuration of the input parameters, we managed to improve the overall F-score compared to previous work, while another configuration yielded improved precision (96%) at a cost of lower recall. Using embedding-based features as a replacement for dictionary-based features provides a significant benefit: while a large bilingual parallel corpus is required to generate the Giza++ word alignment lists, no such...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Word-embedding based bilingual terminology alignment","attachmentId":84042328,"attachmentType":"pdf","work_url":"https://www.academia.edu/76289705/Word_embedding_based_bilingual_terminology_alignment","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/76289705/Word_embedding_based_bilingual_terminology_alignment"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="83431551" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/83431551/Learning_Contextualised_Cross_lingual_Word_Embeddings_and_Alignments_for_Extremely_Low_Resource_Languages_Using_Parallel_Corpora">Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="36652369" href="https://independent.academia.edu/YujiMatsumoto">Yuji Matsumoto</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 1st Workshop on Multilingual Representation Learning, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence. Through sharing model parameters among different languages, our model jointly trains the word embeddings in a common cross-lingual space. We also propose to combine word and subword embeddings to make use of orthographic similarities across different languages. We base our experiments on real-world data from endangered languages, namely Yongning Na, Shipibo-Konibo, and Griko. Our experiments on bilingual lexicon induction and word alignment tasks show that our model outperforms existing methods by a large margin for most language pairs. These results demonstrate that, contrary to common belief, an encoder-decoder translation model is beneficial for learning crosslingual representations even in extremely lowresource conditions. Furthermore, our model also works well on high-resource conditions, achieving state-of-the-art performance on a German-English word-alignment task. 1</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora","attachmentId":88775525,"attachmentType":"pdf","work_url":"https://www.academia.edu/83431551/Learning_Contextualised_Cross_lingual_Word_Embeddings_and_Alignments_for_Extremely_Low_Resource_Languages_Using_Parallel_Corpora","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/83431551/Learning_Contextualised_Cross_lingual_Word_Embeddings_and_Alignments_for_Extremely_Low_Resource_Languages_Using_Parallel_Corpora"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":72880713,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":72880713,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_72880713" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="53093447" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/53093447/A_Comparison_of_Architectures_and_Pretraining_Methods_for_Contextualized_Multilingual_Word_Embeddings">A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="64162417" href="https://independent.academia.edu/NielsvanderHeijden">Niels van der Heijden</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the AAAI Conference on Artificial Intelligence</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings","attachmentId":70042199,"attachmentType":"pdf","work_url":"https://www.academia.edu/53093447/A_Comparison_of_Architectures_and_Pretraining_Methods_for_Contextualized_Multilingual_Word_Embeddings","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/53093447/A_Comparison_of_Architectures_and_Pretraining_Methods_for_Contextualized_Multilingual_Word_Embeddings"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="1" data-entity-id="111321836" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/111321836/Weakly_Supervised_Concept_based_Adversarial_Learning_for_Cross_lingual_Word_Embeddings">Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="24919989" href="https://unige.academia.edu/paolamerlo">paola merlo</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2019</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings","attachmentId":108892753,"attachmentType":"pdf","work_url":"https://www.academia.edu/111321836/Weakly_Supervised_Concept_based_Adversarial_Learning_for_Cross_lingual_Word_Embeddings","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/111321836/Weakly_Supervised_Concept_based_Adversarial_Learning_for_Cross_lingual_Word_Embeddings"><span class="ds2-5-text-link__content">View PDF</span><span 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class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="3" data-entity-id="71154666" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/71154666/Learning_Multilingual_Embeddings_for_Cross_Lingual_Information_Retrieval_in_the_Presence_of_Topically_Aligned_Corpora">Learning Multilingual Embeddings for Cross-Lingual Information Retrieval in the Presence of Topically Aligned Corpora</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="4590" href="https://iitk.academia.edu/ArnabBhattacharya">Arnab Bhattacharya</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2018</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Learning 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