This paper proposes a hierarchical attentional neural translation model which focuses on enhancing source-side hierarchical representations by covering both local and global semantic information using a bidirectional tree-based encoder. To maximize the predictive likelihood of target words, a weighted variant of an attention mechanism is used to balance the attentive information between lexical and phrase vectors. Using a tree-based rare word encoding, the proposed model is extended to sub-word level to alleviate the out-of-vocabulary (OOV) problem. Empirical results reveal that the proposed model significantly outperforms sequence-to-sequence attention-based and tree-based neural translation models in English-Chinese translation tasks.
CITATION STYLE
Yang, B., Wong, D. F., Xiao, T., Chao, L. S., & Zhu, J. (2017). Towards bidirectional hierarchical representations for attention-based neural machine translation. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 1432–1441). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d17-1150
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