Sentence correction incorporating relative position and parse template language models

  • Authors:

  • Chung-Hsien Wu;Chao-Hong Liu;Matthew Harris;Liang-Chih Yu

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan;Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan;Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan;Department of Information Management, Yuan Ze University, Chung-Li, Taiwan

  • Venue:
  • IEEE Transactions on Audio, Speech, and Language Processing
  • Year:
  • 2010

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Abstract

Sentence correction has been an important emerging issue in computer-assisted language learning. However, existing techniques based on grammar rules or statistical machine translation are still not robust enough to tackle the common errors in sentences produced by second language learners. In this paper, a relative position language model and a parse template language model are proposed to complement traditional language modeling techniques in addressing this problem. A corpus of erroneous English-Chinese language transfer sentences along with their corrected counterparts is created and manually judged by human annotators. Experimental results show that compared to a state-of-the-art phrase-based statistical machine translation system, the error correction performance of the proposed approach achieves a significant improvement using human evaluation.