Reranking bilingually extracted paraphrases using monolingual distributional similarity

  • Authors:
  • Tsz Ping Chan;Chris Callison-Burch;Benjamin Van Durme

  • Affiliations:
  • Johns Hopkins University;Johns Hopkins University;Johns Hopkins University

  • Venue:
  • GEMS '11 Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics
  • Year:
  • 2011

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Abstract

This paper improves an existing bilingual paraphrase extraction technique using monolingual distributional similarity to rerank candidate paraphrases. Raw monolingual data provides a complementary and orthogonal source of information that lessens the commonly observed errors in bilingual pivot-based methods. Our experiments reveal that monolingual scoring of bilingually extracted paraphrases has a significantly stronger correlation with human judgment for grammaticality than the probabilities assigned by the bilingual pivoting method does. The results also show that monolingual distribution similarity can serve as a threshold for high precision paraphrase selection.