Bering Lab’s Submissions on WAT 2021 Shared Task (2021)

Abstract: This paper presents the Bering Lab’s submission to the shared tasks of the 8th Workshop on Asian Translation (WAT 2021) on JPC2 and NICT-SAP. We participated in all tasks on JPC2 and IT domain tasks on NICT-SAP. Our approach for all tasks mainly focused on building NMT systems in domain-specific corpora. We crawled patent document pairs for English-Japanese, Chinese-Japanese, and Korean-Japanese. After cleaning noisy data, we built parallel corpus by aligning those sentences with the sentence-level similarity scores. Also, for SAP test data, we collected the OPUS dataset including three IT domain corpora. We then trained transformer on the collected dataset. Our submission ranked 1st in eight out of fourteen tasks, achieving up to an improvement of 2.87 for JPC2 and 8.79 for NICT-SAP in BLEU score .

## introduction the wat 2021 shared task @xcite 1 focuses a comprehensive set of machine translations on asian languages. they gather and share the resources and knowledge about asian language translation through a variety of tasks on the broad topics such as documentlevel translation, multi-modal translation, and domain adaptation. among those tasks, we participated on two tasks: (1) jpo patent corpus (jpc2), a translation task on patent corpus of japanese ↔ english/korean/chinese, and (2) nict-sap it domain, a translation task on software documentation corpus of english ↔ hindi/indonesian/malaysian/thai. according to the our approach begins with collecting rich and clean sentence pairs from web and public dataset. for jpc2, we crawled the patent documents from web for each language pairs then built parallel corpus by pairing each sentence with the similarity scores between source and target sentence representation vectors. for nict-sap it domain, we collected public dataset, opus @xcite , and weighted the it corpus among those corpus while training. in addition to the rich and clean additional corpus, we chose transformer @xcite , broadly recognized as a strong machine translation system. our method obtained the new state-of-the-art results on four out of six jpc2 tasks, especially amounting to 2.87 absolute improvement on bleu scores for japanese to korean translation. to validate the effect of the additional data, we conducted the ablation study on @xmath0. furthermore, our models ranked first place on four out of eight nict-sap it domain tasks, achieving 8.79 improvement for indonesian to english. ## task description we participate jpo patent corpus (jpc2) and sap's it translation tasks. ## system overview in this section we introduce our approach for two tasks. ## conclusion in this work, we described the bering lab's submission to the wat 2021 shared tasks. we collected the in-domain dataset for both jpc2 and nict-sap tasks and built transformer-based mt systems on those corpora. which were trained on given train dataset and additional crawled patent data. our models ranked first place in eight out of fourteen tasks, amounting a high improvements for both tasks.