In this paper, we describe our NAIST-NICT submission to the WMT’23 English ↔ Japanese general machine translation task. Our system generates diverse translation candidates and reranks them using a two-stage reranking system to find the best translation. First, we generated 50 candidates each from 18 translation methods using a variety of techniques to increase the diversity of the translation candidates. We trained seven models per language direction using various combinations of hyperparameters. From these models we used various decoding algorithms, ensembling the models, and using kNN-MT (Khandelwal et al., 2021). We processed the 900 translation candidates through a two-stage reranking system to find the most promising candidate. In the first step, we compared 50 candidates from each translation method using DrNMT (Lee et al., 2021) and returned the candidate with the best score. We ranked the final 18 candidates using COMET-MBR (Fernandes et al., 2022) and returned the best score as the system output. We found that generating diverse translation candidates improved translation quality using the well-designed reranker model.

In this paper, we describe our NAIST-NICT-TIT submission to the WMT22 general machine translation task. We participated in this task for the English ↔ Japanese language pair. Our system is characterized as an ensemble of Transformer big models, k-nearest-neighbor machine translation (kNN-MT) (Khandelwal et al., 2021), and reranking.In our translation system, we construct the datastore for kNN-MT from back-translated monolingual data and integrate kNN-MT into the ensemble model. We designed a reranking system to select a translation from the n-best translation candidates generated by the translation system. We also use a context-aware model to improve the document-level consistency of the translation.

We introduce a Cyrillic-to-Latin transliterator for the Tatar language based on subword-level language identification. The transliteration is a challenging task due to the following two reasons. First, because modern Tatar texts often contain intra-word code-switching to Russian, a different transliteration set of rules needs to be applied to each morpheme depending on the language, which necessitates morpheme-level language identification. Second, the fact that Tatar is a low-resource language, with most of the texts in Cyrillic, makes it difficult to prepare a sufficient dataset. Given this situation, we proposed a transliteration method based on subword-level language identification. We trained a language classifier with monolingual Tatar and Russian texts, and applied different transliteration rules in accord with the identified language. The results demonstrate that our proposed method outscores other Tatar transliteration tools, and imply that it correctly transcribes Russian loanwords to some extent.