Tomoya Mizumoto

Discriminative Reranking for Grammatical Error Correction with Statistical Machine Translation

Research on grammatical error correction has received considerable attention. For dealing with all types of errors, grammatical error correction methods that employ statistical machine translation (SMT) have been proposed in recent years. An SMT system generates candidates with scores for all candidates and selects the sentence with the highest score as the correction result. However, the 1-best result of an SMT system is not always the best result. Thus, we propose a reranking approach for grammatical error correction. The reranking approach is used to re-score N-best results of the SMT and reorder the results. Our experiments show that our reranking system using parts of speech and syntactic features improves performance and achieves state-of-theart quality, with an F0.5 score of 40.0.

Grammatical Error Correction Considering Multi-word Expressions

Multi-word expressions (MWEs) have been recognized as important linguistic information and much research has been conducted especially on their extraction and interpretation. On the other hand, they have hardly been used in real application areas. While those who are learning English as a second language (ESL) use MWEs in their writings just like native speakers, MWEs haven’t been taken into consideration in grammatical error correction tasks. In this paper, we investigate the grammatical error correction method using MWEs. Our method proposes a straightforward application of MWEs to grammatical error correction, but experimental results show that MWEs have a beneficial effect on grammatical error correction.

The chanty bear: a new application for hri research

This paper presents yet another English-teaching robot, while putting emphasis on the merits which are offered by second language education to human robot interaction (HRI) research. The chanty bear, our prototype robot based on a rhythmic teaching method of English called Jazz Chants is introduced.