Word-Based Domain Feature-Sensitive Multi-domain Neural Machine Translation

The accuracy of the existing word-based domain feature learning methods on domain discrimination is still low and the further research for domain feature learning is required. In order to improve domain discrimination and provide accurate translation, this paper proposes a word-based domain feature-sensitive learning mechanism, including 1) the context feature encoding at encoder side, to widen the study range of word-based domain features, introducing convolutional neural networks (CNN) in encoder for extracting features from word strings with different lengths in parallel as word context features; and 2) enhanced domain feature learning. A domain discriminator module based on multi-layer perceptions (MLP) is designed to enhance the learning ability of obtaining more accurate domain proportion from word context features and improve the accuracy of word domain discrimination. Experiments on English-Chinese task of UM-Corpus and English-French task of OPUS show that the average BLEU scores of the proposed method exceed the strong baseline by 0.82 and 1.06 respectively. The accuracy of domain discrimination is improved by 10.07% and 18.06% compared with the baseline. More studies illustrate that the improvements of average BLEU scores and accuracy of domain discrimation are contributed by the proposed word-based domain feature-sensitive learning mechanism. © 2023 Peking University. All rights reserved.