An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation

TL;DR

This paper introduces a new domain adaptation technique for neural machine translation called 'mixed fine tuning', which combines fine tuning and multi-domain training with artificial tags, and compares it empirically to existing methods.

Contribution

The paper presents a novel domain adaptation method for NMT that integrates fine tuning and multi-domain training with artificial tags, and provides an empirical comparison with existing approaches.

Findings

Mixed fine tuning improves translation quality over baseline methods.

Artificial tags effectively indicate domain-specific information.

The method has certain limitations in specific domain scenarios.

Abstract

In this paper, we propose a novel domain adaptation method named "mixed fine tuning" for neural machine translation (NMT). We combine two existing approaches namely fine tuning and multi domain NMT. We first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus which is a mix of the in-domain and out-of-domain corpora. All corpora are augmented with artificial tags to indicate specific domains. We empirically compare our proposed method against fine tuning and multi domain methods and discuss its benefits and shortcomings.