HW - TSC ’s Submissions to the WMT 21 Biomedical Translation Task (2021)

Abstract: This paper describes the submission of Huawei Translation Service Center (HW-TSC) to WMT21 biomedical translation task in two language pairs: Chinese↔English and German↔English (Our registered team name is HuaweiTSC). Technical details are introduced in this paper, including model framework, data pre-processing method and model enhancement strategies. In addition, using the wmt20 OK-aligned biomedical test set, we compare and analyze system performances under different strategies. On WMT21 biomedical translation task, Our systems in English→Chinese and English→German directions get the highest BLEU scores among all submissions according to the official evaluation results.

## introduction we have witnessed great progress made by neural machine translations @xcite in recent years. however, domain adaptation remains to be a tough issue. as noted by @xcite , translations by nmt systems in out-ofdomain scenarios are relatively poor, and highquality data in specific domains are difficult to obtain, which pose great challenges to certain translation tasks (e.g. biomedical translation). to address the domain adaptation issue, on one hand, we leverage data diversification @xcite , forward translation @xcite and back translation @xcite to generate synthetic in-domain corpora. on the other hand, fine-tuning @xcite and ensemble @xcite are used to further enhance system performances in the biomedical domain. we introduce our data strategy in section 2, and model architectures as well as model enhancement techniques in section 3. section 4 presents experimental results of both language pairs on the wmt20 ok-aligned biomedical test set. section 5 is a conclusion of our work. ## experimental result we train baseline models using wmt21 news data, then incrementally train them using medical bilingual corpora and synthetic data generated by data augmentation techniques, fine-tune models with previous years' test sets, and finally ensemble multiple models to produce submitted results. we benchmark our submissions using the wmt20 okalign test set. bleu scores are calculated using the mteval script from moses @xcite . the results are shown in ## conclusion this paper presents the submissions of hw-tsc to the wmt21 biomedical translation task. we perform experiments with a series of pre-processing and training strategies. the effectiveness of each strategy is demonstrated by our experiment results. combining with data augmentation strategies, incremental training with in-domain data on the basis of a baseline model from new domain can effectively improve in-domain translation quality. our systems in @xmath1.