Paper: Factored Statistical Machine Translation for Grammatical Error Correction

ACL ID W14-1711
Title Factored Statistical Machine Translation for Grammatical Error Correction
Venue International Conference on Computational Natural Language Learning
Session shared task
Year 2014
Authors

This paper describes our ongoing work on grammatical error correction (GEC). Focusing on all possible error types in a real-life environment, we propose a factored statistical machine translation (SMT) model for this task. We consider error correction as a series of language translation problems guided by various linguistic information, as factors that influence translation results. Factors included in our study are morphological information, i.e. word stem, prefix, suffix, and Part-of-Speech (PoS) information. In addition, we also experimented with different combinations of translation models (TM), phrase-based and factor-based, trained on various datasets to boost the overall performance. Empirical results show that the proposed model yields an improvement of 32.54% over...