Tomoshige Kiyuna


pdf bib
Machine Translation with Pre-specified Target-side Words Using a Semi-autoregressive Model
Seiichiro Kondo | Aomi Koyama | Tomoshige Kiyuna | Tosho Hirasawa | Mamoru Komachi
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

We introduce our TMU Japanese-to-English system, which employs a semi-autoregressive model, to tackle the WAT 2021 restricted translation task. In this task, we translate an input sentence with the constraint that some words, called restricted target vocabularies (RTVs), must be contained in the output sentence. To satisfy this constraint, we use a semi-autoregressive model, namely, RecoverSAT, due to its ability (known as “forced translation”) to insert specified words into the output sentence. When using “forced translation,” the order of inserting RTVs is a critical problem. In this work, we aligned the source sentence and the corresponding RTVs using GIZA++. In our system, we obtain word alignment between a source sentence and the corresponding RTVs and then sort the RTVs in the order of their corresponding words or phrases in the source sentence. Using the model with sorted order RTVs, we succeeded in inserting all the RTVs into output sentences in more than 96% of the test sentences. Moreover, we confirmed that sorting RTVs improved the BLEU score compared with random order RTVs.


pdf bib
Construction of an Evaluation Corpus for Grammatical Error Correction for Learners of Japanese as a Second Language
Aomi Koyama | Tomoshige Kiyuna | Kenji Kobayashi | Mio Arai | Mamoru Komachi
Proceedings of the Twelfth Language Resources and Evaluation Conference

The NAIST Lang-8 Learner Corpora (Lang-8 corpus) is one of the largest second-language learner corpora. The Lang-8 corpus is suitable as a training dataset for machine translation-based grammatical error correction systems. However, it is not suitable as an evaluation dataset because the corrected sentences sometimes include inappropriate sentences. Therefore, we created and released an evaluation corpus for correcting grammatical errors made by learners of Japanese as a Second Language (JSL). As our corpus has less noise and its annotation scheme reflects the characteristics of the dataset, it is ideal as an evaluation corpus for correcting grammatical errors in sentences written by JSL learners. In addition, we applied neural machine translation (NMT) and statistical machine translation (SMT) techniques to correct the grammar of the JSL learners’ sentences and evaluated their results using our corpus. We also compared the performance of the NMT system with that of the SMT system.