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

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.

arXiv comment6 pages

References in corpus (4)

• Sequence to Sequence Learning with Neural Networks
• Fast Domain Adaptation for Neural Machine Translation
• Domain specialization: a post-training domain adaptation for Neural Machine Translation
• Domain Control for Neural Machine Translation

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• Structure-Invariant Testing for Machine Translation
• Robust Machine Translation with Domain Sensitive Pseudo-Sources: Baidu-OSU WMT19 MT Robustness Shared Task System Report
• Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation
• Domain Adaptation of NMT models for English-Hindi Machine Translation Task at AdapMT ICON 2020
• Testing Machine Translation via Referential Transparency
• Don't Go Far Off: An Empirical Study on Neural Poetry Translation