Iterative Constrained Back-Translation for Unsupervised Domain Adaptation of Machine Translation (2022)

Abstract: Back-translation has been proven to be effective in unsupervised domain adaptation of neural machine translation (NMT). However, the existing back-translation methods mainly improve domain adaptability by generating in-domain pseudo-parallel data that contains sentence-structural knowledge, paying less attention to the in-domain lexical knowledge, which may lead to poor translation of unseen in-domain words. In this paper, we propose an Iterative Constrained Back-Translation (ICBT) method to incorporate in-domain lexical knowledge on the basis of BT for unsupervised domain adaptation of NMT. Specifically, we apply lexical constraints into back-translation to generate pseudo-parallel data with in-domain lexical knowledge, and then perform round-trip iterations to incorporate more lexical knowledge. Based on this, we further explore sampling strategies of constrained words in ICBT to introduce more targeted lexical knowledge, via domain specificity and confidence estimation. Experimental results on four domains show that our approach achieves state-of-the-art results, improving the BLEU score by up to 3.08 compared to the strongest baseline, which demonstrates the effectiveness of our approach.

## introduction neural machine translation (nmt) has made breakthroughs in resource-rich domains @xcite , which requires abundant in-domain parallel data @xcite . unfortunately, there is no enough parallel data for many domains, while the monolingual corpus is much easier to obtain. therefore, unsupervised domain adaptation of nmt, which aims to improve in-domain translation through out-ofdomain parallel corpus and in-domain monolingual specifying the hong kong mortgage securities limited as a public sector in hong kong. ## our approach the iterative constrained back-translation (icbt) method proposed in this paper aims to introduce the lexical knowledge from monolingual data into back-translation. in this section, we first describe how to constrain back-translation and perform round-trip iterations ( § 3.1), and then we introduce two strategies for sampling more targeted constrained words ( § 3.2). ## conclusion and future work this paper proposes a method for unsupervised domain adaptation of nmt named iterative constrained back-translation (icbt), in which lexical constraints are applied to back-translation, aiming to incorporate in-domain lexical knowledge into synthetic parallel data from bt. besides, we propose two strategies for sampling constraints to exert more targeted constraints. we conduct experiments on english-chinese translation tasks in four domains. the experiments show that our method can introduce beneficial lexical knowledge to bt, thus achieving state-of-the-art results. we believe that the lexical constraint is not only suitable for unsupervised domain adaptation, but also promising in the semi-supervised scenario with a small amount of in-domain parallel corpus. in the future, we will explore the application of lexical constraints in supervised or semi-supervised domain adaptation of nmt.