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.

Wordnets play an important role not only in linguistics but also in natural language processing (NLP). This paper reports major results of a project which aims to construct a wordnet for Vietnamese language. We propose a two-phase approach to the construction of Vietnamese WordNet employing available language resources and ensuring Vietnamese specific linguistic and cultural characteristics. We also give statistical results and analyses to show characteristics of the wordnet.