Neural Machine Translation with CARU-Embedding Layer and CARU-Gated Attention Layer

The attention mechanism performs well for the Neural Machine Translation (NMT) task, but heavily depends on the context vectors generated by the attention network to predict target words. This reliance raises the issue of long-term dependencies. Indeed, it is very common to combine predicates with postpositions in sentences, and the same predicate may have different meanings when combined with different postpositions. This usually poses an additional challenge to the NMT study. In this work, we observe that the embedding vectors of different target tokens can be classified by part-of-speech, thus we analyze the Natural Language Processing (NLP) related Content-Adaptive Recurrent Unit (CARU) unit and apply it to our attention model (CAAtt) and embedding layer (CAEmbed). By encoding the source sentence with the current decoded feature through the CARU, CAAtt is capable of achieving translation content-adaptive representations, which attention weights are contributed and enhanced by our proposed L 1 exp N x normalization. Furthermore, CAEmbed aims to alleviate long-term dependencies in the target language through partial recurrent design, performing the feature extraction in a local perspective. Experiments on the WMT14, WMT17, and Multi30k translation tasks show that the proposed model achieves improvements in BLEU scores and enhancement of convergence over the attention-based plain NMT model. We also investigate the attention weights generated by the proposed approaches, which indicate that refinement over the different combinations of adposition can lead to different interpretations. Specifically, this work provides local attention to some specific phrases translated in our experiment. The results demonstrate that our approach is effective in improving performance and achieving a more reasonable attention distribution compared to the state-of-the-art models.