Abstract

This paper describes the machine translation systems developed by the Universidade Federal do Rio Grande do Sul (UFRGS) team for the biomedical translation shared task. Our systems are based on statistical machine translation and neural machine translation, using the Moses and OpenNMT toolkits, respectively. We participated in four translation directions for the English/Spanish and English/Portuguese language pairs. To create our training data, we concatenated several parallel corpora, both from in-domain and out-of-domain sources, as well as terminological resources from UMLS. Our systems achieved the best BLEU scores according to the official shared task evaluation.

Highlights

  • We present the system developed at the Universidade Federal do Rio Grande do Sul (UFRGS) for the Biomedical Translation shared task in the Third Conference on Machine Translation (WMT18), which consists in translating scientific texts from the biological and health domain

  • out of vocabulary (OOV) words were replace by their original word in the source language, all other OpenNMT options for translation were kept as default

  • We detail the results achieved by our statistical machine translation (SMT) and neural machine translation (NMT) systems on the official test data used in the shared task

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Summary

Introduction

We present the system developed at the Universidade Federal do Rio Grande do Sul (UFRGS) for the Biomedical Translation shared task in the Third Conference on Machine Translation (WMT18), which consists in translating scientific texts from the biological and health domain. In this edition of the shared task, six language pairs are considered: English/Chinese, English/French, English/German, English/Portuguese, English/Romanian, and English/Spanish. Our participation in this task considered the English/Portuguese and English/Spanish language pairs, with translations in both directions.

Related Works
Corpora
Terminological Resources
Pre-processing
SMT System
NMT System
Experimental Results
Conclusions

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