Monolingual distributional similarity for text-to-text generation

  • Authors:

  • Juri Ganitkevitch;Benjamin Van Durme;Chris Callison-Burch

  • Affiliations:

  • Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University, Baltimore, MD;Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University, Baltimore, MD;Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University, Baltimore, MD

  • Venue:
  • SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
  • Year:
  • 2012

  • Annotated Gigaword

    AKBC-WEKEX '12 Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction

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Abstract

Previous work on paraphrase extraction and application has relied on either parallel datasets, or on distributional similarity metrics over large text corpora. Our approach combines these two orthogonal sources of information and directly integrates them into our paraphrasing system's log-linear model. We compare different distributional similarity feature-sets and show significant improvements in grammaticality and meaning retention on the example text-to-text generation task of sentence compression, achieving state-of-the-art quality.