Adapting Neural Machine Translation with Parallel Synthetic Data

Abstract

Recent works have shown that the usage of a synthetic parallel corpus can be effectively exploited by a neural machine translation system. In this paper, we propose a new method for adapting a general neural machine translation system to a specific task, by exploiting synthetic data. The method consists in selecting, from a large monolingual pool of sentences in the source language, those instances that are more related to a given test set. Next, this selection is automatically translated and the general neural machine translation sys-tem is fine-tuned with these data. For evaluating the adaptation method, we first conducted experiments in two controlled domains, with common and well-studied corpora. Then, we evaluated our proposal on a real e-commerce task, yielding consistent improvements in terms of translation quality.