Conference paper Open Access

Efficient Bilingual Generalization from Neural Transduction Grammar Induction

Yan, Yuchen; Wu, Dekai; Kumyol, Serkan


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  <identifier identifierType="DOI">10.5281/zenodo.3524973</identifier>
  <creators>
    <creator>
      <creatorName>Yan, Yuchen</creatorName>
      <givenName>Yuchen</givenName>
      <familyName>Yan</familyName>
      <affiliation>Department of Computer Science and Engineering, Human Language Technology Center, The Hong Kong University of Science and Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Wu, Dekai</creatorName>
      <givenName>Dekai</givenName>
      <familyName>Wu</familyName>
      <affiliation>Department of Computer Science and Engineering, Human Language Technology Center, The Hong Kong University of Science and Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Kumyol, Serkan</creatorName>
      <givenName>Serkan</givenName>
      <familyName>Kumyol</familyName>
      <affiliation>Department of Computer Science and Engineering, Human Language Technology Center, The Hong Kong University of Science and Technology</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Efficient Bilingual Generalization from Neural Transduction Grammar Induction</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-11-02</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3524973</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3524972</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/iwslt2019</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;We introduce (1) a novel neural network structure for bilingual modeling of sentence pairs that allows efficient capturing of bilingual relationship via biconstituent composition, (2) the concept of neural network biparsing, which applies to not only machine translation (MT) but also to a variety of other bilingual research areas, and (3) the concept of a biparsing-backpropagation training loop, which we hypothesize that can efficiently learn complex biparse tree patterns. Our work distinguishes from sequential attention-based models, which are more traditionally found in neural machine translation (NMT) in three aspects. First, our model enforces compositional constraints. Second, our model has a smaller search space in terms of discovering bilingual relationships from bilingual sentence pairs. Third, our model produces explicit biparse trees, which enable transparent error analysis during evaluation and external tree constraints during training.&lt;/p&gt;</description>
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