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Closing the Gap: Domain Adaptation from Explicit to Implicit Discourse Relations

Yangfeng Ji,Gongbo Zhang,Jacob Eisenstein

Published 2015 in Conference on Empirical Methods in Natural Language Processing

ABSTRACT

Many discourse relations are explicitly marked with discourse connectives, and these examples could potentially serve as a plentiful source of training data for recognizing implicit discourse relations. However, there are important linguistic differences between explicit and implicit discourse relations, which limit the accuracy of such an approach. We account for these differences by applying techniques from domain adaptation, treating implicitly and explicitly-marked discourse relations as separate domains. The distribution of surface features varies across these two domains, so we apply a marginalized denoising autoencoder to induce a dense, domain-general representation. The label distribution is also domain-specific, so we apply a resampling technique that is similar to instance weighting. In combination with a set of automatically-labeled data, these improvements eliminate more than 80% of the transfer loss incurred by training an implicit discourse relation classifier on explicitly-marked discourse relations.

PUBLICATION RECORD

  • Publication year

    2015

  • Venue

    Conference on Empirical Methods in Natural Language Processing

  • Publication date

    2015-09-01

  • Fields of study

    Mathematics, Linguistics, Computer Science

  • Identifiers
    DOI 10.18653/v1/D15-1264
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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