Tencent AI Lab Machine Translation Systems for the WMT 20 Biomedical Translation Task (2020)

Abstract: This paper describes the Tencent AI Lab submission of the WMT2020 shared task on biomedical translation in four language directions: German<->English, English<->German, Chinese<->English and English<->Chinese. We implement our system with model ensemble technique on different transformer architectures (Deep, Hybrid, Big, Large Transformers). To enlarge the in-domain bilingual corpus, we use back-translation of monolingual in-domain data in the target language as additional in-domain training data. Our systems in German->English and English->German are ranked 1st and 3rd respectively according to the official evaluation results in terms of BLEU scores.

## introduction neural machine translation @xcite has achieved great progress in recent years. however, as @xcite pointed out, nmt systems suffer from poor translation performance in out-ofdomain scenarios, which poses a great challenge for the biomedical translation task. in this paper, we present our submission to the wmt20 shared task on biomedical translation task. we participated in two language directions: german-english and chinese-english. to address the domain problem, on one hand, we adopt model ensemble technique @xcite with different transformer architectures to build a more robust model. on the other hand, we enlarge the in-domain bilingual corpus with back-translation approach @xcite . our contributions are as follows: the rest of this paper is organized as follows. section 2 presents our system with four different transformer architectures: deep, hybrid, big, large transformers. section 3 describes the training data used in our system, including bilingual data, monolingual data and synthetic bilingual data. section 4 reports experimental results in two language directions. finally, we conclude our work in section 5. ## system in our systems, we adopt four different model architectures with transformer @xcite : ## data the data used to train our system consists of three parts: bilingual data, monolingual data and synthetic bilingual data. ## experiment we report experimental results in four language pairs: german-english (de/en), english-german (en/de), chinese-english (zh/en) and english-chinese (en/zh). ## official results the official automatic evaluation results of our submissions for wmt 2020 are presented in ## conclusion in this paper, we present tencent ai lab machine translation systems for the wmt20 biomedical translation shared task and release the pre-trained models as well as the in-domain synthetic chinese-english bilingual data for the research commu in the future, we plan to explore domain adaptation @xcite @xcite , phrase modeling @xcite , structural modeling @xcite strategies to improve the system performance.