Neural machine translation (NMT) models do not work well in domains different from the training data. The standard approach to this problem is to build a small parallel data in the target domain and perform domain adaptation from a source domain where massive parallel data is available. However, domain adaptation  between distant domains (e.g.,  subtitles and research papers) does not perform effectively because of mismatches in vocabulary;
<coherence> Neural machine translation (NMT) models do not work well in domains different from the training data. The standard approach to this problem is to build a small parallel data in the target domain and perform domain adaptation from a source domain where massive parallel data is available. However, domain adaptation  between distant domains (e.g.,  subtitles and research papers) does not perform effectively because of mismatches in vocabulary;
Neural network methods exhibit strong performance only in a few resource-rich domains. Practitioners, therefore, employ domain adaptation between distant domains (e.g., subtitles and research papers) does not perform effectively because of mismatches in vocabulary;