High frequent in-domain word segmentation and forward translation for the WMT21 Biomedical task

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 subwords in our mixed training set and trained the Transformer model on the generated data. Results show improvements using our proposed method. © 2021 Association for Computational Linguistics