# UCAM Biomedical translation at WMT19: Transfer learning multi-domain ensembles

## TL;DR

This paper presents a transfer learning approach with multi-domain ensembles and adaptive weighting to improve biomedical translation, achieving top results in English-Spanish translation at WMT19.

## Contribution

It introduces a novel combination of transfer learning and ensemble methods with adaptive weighting for biomedical translation.

## Key findings

- Achieved best results on English-Spanish translation at WMT19.
- Demonstrated effectiveness of multi-domain ensembles and adaptive weighting.
- Improved translation quality over baseline models.

## Abstract

The 2019 WMT Biomedical translation task involved translating Medline abstracts. We approached this using transfer learning to obtain a series of strong neural models on distinct domains, and combining them into multi-domain ensembles. We further experiment with an adaptive language-model ensemble weighting scheme. Our submission achieved the best submitted results on both directions of English-Spanish.