Swivel: Improving Embeddings by Noticing What's Missing

<!DOCTYPE html>     <html class="no-js" lang="en">  <head> <title>Swivel: Improving Embeddings by Noticing What's Missing - NASA/ADS</title>  <link rel="apple-touch-icon" sizes="180x180" href="//styles/favicon/apple-touch-icon.png" /> <link rel="icon" type="image/png" sizes="32x32" href="//styles/favicon/favicon-32x32.png" /> <link rel="icon" type="image/png" sizes="16x16" href="//styles/favicon/favicon-16x16.png" /> <link rel="manifest" href="//styles/favicon/site.webmanifest" /> <link rel="mask-icon" href="//styles/favicon/safari-pinned-tab.svg" color="#5bbad5" /> <meta name="apple-mobile-web-app-title" content="NASA ADS" /> <meta name="application-name" content="NASA ADS" /> <meta name="msapplication-TileColor" content="#ffc40d" /> <meta name="theme-color" content="#ffffff" />  <link rel="stylesheet" href="/styles/css/styles.css"> <meta name="robots" content="noarchive"> <link rel="canonical" href="http://ui.adsabs.harvard.edu/abs/2016arXiv160202215S/abstract"/> <meta name="description" content="We present Submatrix-wise Vector Embedding Learner (Swivel), a method for generating low-dimensional feature embeddings from a feature co-occurrence matrix. Swivel performs approximate factorization of the point-wise mutual information matrix via stochastic gradient descent. It uses a piecewise loss with special handling for unobserved co-occurrences, and thus makes use of all the information in the matrix. While this requires computation proportional to the size of the entire matrix, we make use of vectorized multiplication to process thousands of rows and columns at once to compute millions of predicted values. Furthermore, we partition the matrix into shards in order to parallelize the computation across many nodes. This approach results in more accurate embeddings than can be achieved with methods that consider only observed co-occurrences, and can scale to much larger corpora than can be handled with sampling methods.">  <meta property="og:type" content="eprint"> <meta property="og:title" content="Swivel: Improving Embeddings by Noticing What's Missing"> <meta property="og:site_name" content="NASA/ADS"> <meta property="og:description" content="We present Submatrix-wise Vector Embedding Learner (Swivel), a method for generating low-dimensional feature embeddings from a feature co-occurrence matrix. Swivel performs approximate factorization of the point-wise mutual information matrix via stochastic gradient descent. It uses a piecewise loss with special handling for unobserved co-occurrences, and thus makes use of all the information in the matrix. While this requires computation proportional to the size of the entire matrix, we make use of vectorized multiplication to process thousands of rows and columns at once to compute millions of predicted values. Furthermore, we partition the matrix into shards in order to parallelize the computation across many nodes. This approach results in more accurate embeddings than can be achieved with methods that consider only observed co-occurrences, and can scale to much larger corpora than can be handled with sampling methods."> <meta property="og:url" content="https://ui.adsabs.harvard.edu/abs/2016arXiv160202215S/abstract"> <meta property="og:image" content="https://ui.adsabs.harvard.edu/styles/img/transparent_logo.svg"> <meta property="article:published_time" content="02/2016"> <meta property="article:author" content="Shazeer, Noam"> <meta property="article:author" content="Doherty, Ryan"> <meta property="article:author" content="Evans, Colin"> <meta property="article:author" content="Waterson, Chris">  <meta name="citation_journal_title" content="arXiv e-prints"> <meta name="citation_authors" content="Shazeer, Noam;Doherty, Ryan;Evans, Colin;Waterson, Chris"> <meta name="citation_title" content="Swivel: Improving Embeddings by Noticing What's Missing"> <meta name="citation_date" content="02/2016"> <meta name="citation_firstpage" content="arXiv:1602.02215"> <meta name="citation_doi" content="10.48550/arXiv.1602.02215"> <meta name="citation_language" content="en"> <meta name="citation_keywords" content="Computer Science - Computation and Language"> <meta name="citation_abstract_html_url" content="https://ui.adsabs.harvard.edu/abs/2016arXiv160202215S/abstract"> <meta name="citation_publication_date" content="02/2016"> <meta name="citation_arxiv_id" content="arXiv:1602.02215" /> <link title="schema(PRISM)" rel="schema.prism" href="http://prismstandard.org/namespaces/1.2/basic/" /> <meta name="prism.publicationDate" content="02/2016" /> <meta name="prism.publicationName" content="arXiv" /> <meta name="prism.startingPage" content="arXiv:1602.02215" /> <link title="schema(DC)" rel="schema.dc" href="http://purl.org/dc/elements/1.1/" /> <meta name="dc.identifier" content="doi:10.48550/arXiv.1602.02215" /> <meta name="dc.date" content="02/2016" /> <meta name="dc.source" content="arXiv" /> <meta name="dc.title" content="Swivel: Improving Embeddings by Noticing What's Missing" /> <meta name="dc.creator" content="Shazeer, Noam"> <meta name="dc.creator" content="Doherty, Ryan"> <meta name="dc.creator" content="Evans, Colin"> <meta name="dc.creator" content="Waterson, Chris">  <meta name="twitter:card" content="summary_large_image"/> <meta name="twitter:description" content="We present Submatrix-wise Vector Embedding Learner (Swivel), a method for generating low-dimensional feature embeddings from a feature co-occurrence matrix. Swivel performs approximate factorization of the point-wise mutual information matrix via stochastic gradient descent. It uses a piecewise loss with special handling for unobserved co-occurrences, and thus makes use of all the information in the matrix. While this requires computation proportional to the size of the entire matrix, we make use of vectorized multiplication to process thousands of rows and columns at once to compute millions of predicted values. Furthermore, we partition the matrix into shards in order to parallelize the computation across many nodes. This approach results in more accurate embeddings than can be achieved with methods that consider only observed co-occurrences, and can scale to much larger corpora than can be handled with sampling methods."/> <meta name="twitter:title" content="Swivel: Improving Embeddings by Noticing What's Missing"/> <meta name="twitter:site" content="@adsabs"/> <meta name="twitter:domain" content="NASA/ADS"/> <meta name="twitter:image:src" content="https://ui.adsabs.harvard.edu/styles/img/transparent_logo.svg"/> <meta name="twitter:creator" content="@adsabs"/> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <base href="/"> <style> .btn-full-ads { color: #fff !important; background-color: #1a1a1a !important; border-color: #1a1a1a !important; margin-top: 9px !important; padding-bottom: 10px !important; padding-top: 10px !important; } .btn-full-ads:hover, .btn-full-ads:focus, .btn-full-ads:active, .btn-full-ads.active, 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