High Frequent In-domain Words Segmentation and Forward Translation for the WMT 21 Biomedical Task (2021)

Abstract: This paper reports the optimization of using the out-of-domain data in the Biomedical translation task. We firstly optimized our parallel training dataset using the BabelNet in-domain terminology words. Afterward, to increase the training set, we studied the effects of the out-of-domain data on biomedical translation tasks, and we created a mixture of in-domain and out-of-domain training sets and added more in-domain data using forward translation in the English-Spanish task. Finally, with a simple bpe optimization method, we increased the number of in-domain sub-words in our mixed training set and trained the Transformer model on the generated data. Results show improvements using our proposed method.

## introduction domain adaptation is one of the known challenges in machine translation since nmt(neural machine translation) models are susceptible to the training data @xcite . to say, nmt models perform poorly for domain-specific translation when trained on large out-resource data @xcite . as a result, due to the limitations of specific domain data, domain adaptation strategies help nmt models by increasing the parallel corpora. there have been several tasks to address domain adaptation which recently, in @xcite they proposed a vocabulary adaptation to fine-tune the embedding layers of the nmt model by projecting general word embeddings induced from monolingual data in a target domain onto a source-domain embedding space to improve translation score. on the other hand, augmenting bilingual training data with forwarding and backward translation improves the in-domain translation quality @xcite . inspired by mentioned ideas, in this work, we implemented our strategy by two essential steps: 1) collecting and augmenting data by forwarding translation and then tuning it 041 using babelnet to include biomedical sentences 2) 042 implementing subwords bpe optimization on the 043 train set to study the adaptation of out-of-domain 044 data in the biomedical task. after that, we selected 045 the transformer model @xcite to 046 train our system in different experimental settings. ## experiments experiments illustrated in this section study the effects of the out-of-domain data on indomain(biomedical) translation task as well as the possibility of adapting it by performing a tuned subword-bpe segmentation algorithm 3 to improve the translation quality. we split this section into four parts which start with data collection and prepossessing. then, we describe the training system and, finally, the evaluation scores of the competition.