Application-driven statistical paraphrase generation

  • Authors:
  • Shiqi Zhao;Xiang Lan;Ting Liu;Sheng Li

  • Affiliations:
  • Information Retrieval Lab, Harbin Institute of Technology, Nangang District, Harbin, China;Information Retrieval Lab, Harbin Institute of Technology, Nangang District, Harbin, China;Information Retrieval Lab, Harbin Institute of Technology, Nangang District, Harbin, China;Information Retrieval Lab, Harbin Institute of Technology, Nangang District, Harbin, China

  • Venue:
  • ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
  • Year:
  • 2009

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Abstract

Paraphrase generation (PG) is important in plenty of NLP applications. However, the research of PG is far from enough. In this paper, we propose a novel method for statistical paraphrase generation (SPG), which can (1) achieve various applications based on a uniform statistical model, and (2) naturally combine multiple resources to enhance the PG performance. In our experiments, we use the proposed method to generate paraphrases for three different applications. The results show that the method can be easily transformed from one application to another and generate valuable and interesting paraphrases.