Improving Domain Adaptation Translation with Domain Invariant and Specific Information
arXiv:1904.03879
Abstract
In domain adaptation for neural machine translation, translation performance can benefit from separating features into domain-specific features and common features. In this paper, we propose a method to explicitly model the two kinds of information in the encoder-decoder framework so as to exploit out-of-domain data in in-domain training. In our method, we maintain a private encoder and a private decoder for each domain which are used to model domain-specific information. In the meantime, we introduce a common encoder and a common decoder shared by all the domains which can only have domain-independent information flow through. Besides, we add a discriminator to the shared encoder and employ adversarial training for the whole model to reinforce the performance of information separation and machine translation simultaneously. Experiment results show that our method can outperform competitive baselines greatly on multiple data sets.
11 pages, accepted by NAACL 2019
References in corpus (6)
- Sequence to Sequence Learning with Neural Networks
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- On Using Monolingual Corpora in Neural Machine Translation
- An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation
- Guided Alignment Training for Topic-Aware Neural Machine Translation
- Refining Source Representations with Relation Networks for Neural Machine Translation