PREFER: using a graph-based approach to generate paraphrases for language learning

  • Authors:

  • Mei-Hua Chen;Shih-Ting Huang;Chung-Chi Huang;Hsien-Chin Liou;Jason S. Chang

  • Affiliations:

  • National Tsing Hua University, Taiwan, R.O.C.;National Tsing Hua University, Taiwan, R.O.C.;National Tsing Hua University, Taiwan, R.O.C.;National Tsing Hua University, Taiwan, R.O.C.;National Tsing Hua University, Taiwan, R.O.C.

  • Venue:
  • Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
  • Year:
  • 2012

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Abstract

Paraphrasing is an important aspect of language competence; however, EFL learners have long had difficulty paraphrasing in their writing owing to their limited language proficiency. Therefore, automatic paraphrase suggestion systems can be useful for writers. In this paper, we present PREFER, a paraphrase reference tool for helping language learners improve their writing skills. In this paper, we attempt to transform the paraphrase generation problem into a graphical problem in which the phrases are treated as nodes and translation similarities as edges. We adopt the PageRank algorithm to rank and filter the paraphrases generated by the pivot-based paraphrase generation method. We manually evaluate the performance of our method and assess the effectiveness of PREFER in language learning. The results show that our method successfully preserves both the semantic meaning and syntactic structure of the query phrase. Moreover, the students' writing performance improve most with the assistance of PREFER.