# UFRGS Participation on the WMT Biomedical Translation Shared Task

## TL;DR

This paper details UFRGS's development of statistical and neural machine translation systems for biomedical language pairs, achieving top BLEU scores in the WMT shared task.

## Contribution

The paper introduces combined statistical and neural translation systems utilizing diverse corpora and terminological resources for biomedical translation.

## Key findings

- Achieved best BLEU scores in the shared task
- Utilized combined in-domain and out-of-domain data
- Integrated UMLS terminological resources

## 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.