Statistical Machine Translation Adding Rule-based Machine Translation.

TL;DR: From the results of experiments, the proposed method was effective for the IntRinsicJE and Intrinsic-EJ task, and the future study will try to improve the performance by optimizing parameters.

Abstract: We have developed a two-stage machine translation (MT) system. The first stage is a rule-based machine translation system. The second stage is a normal statistical machine translation system. For Japanese-English machine translation, first, we used a JapaneseEnglish rule-based MT, and we obtained "ENGLISH" sentences from Japanese sentences. Second, we used a standard statistical machine translation. This means that we translated "ENGLISH" to English machine translation. We believe this method has two advantages. One is that there are fewer unknown words. The other is that it produces structured or grammatically correct sentences. From the results of experiments, we obtained a BLEU score of 0.2565 in the Intrinsic-JE task using our proposed method. In contrast, we obtained a BLEU score of 0.2165 in the Intrinsic-JE task using a standard method (moses). And we obtained a BLEU score of 0.2602 in the Intrinsic-EJ task using our proposed method. In contrast, we obtained a BLEU score of 0.2501 in the Intrinsic-EJ task using a standard method (moses). This means that our proposed method was effective for the IntrinsicJE and Intrinsic-EJ task. For the future study, we will try to improve the performance by optimizing parameters.