Using a Grammar Checker for Evaluation and Postprocessing of Statistical Machine Translation

Abstract

One problem in statistical machine translation (SMT) is that the output often is ungrammatical. To address this issue, we have investigated the use of a grammar checker for two purposes in connection with SMT: as an evaluation tool and as a postprocessing tool. To assess the feasibility of the grammar checker on SMT output, we performed an error analysis, which showed that the precision of error identification in general was higher on SMT output than in previous studies on human texts. Using the grammar checker as an evaluation tool gives a complementary picture to standard metrics such as Bleu, which do not account well for grammaticality. We use the grammar checker as a postprocessing tool by automatically applying the error correction suggestions it gives. There are only small overall improvements of the postprocessing on automatic metrics, but the sentences that are affected by the changes are improved, as shown both by automatic metrics and by a human error analysis. These results indicate that grammar checker techniques are a useful complement to SMT.

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