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        <namePart type="given">Baosong</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Towards Bidirectional Hierarchical Representations for Attention-based Neural Machine Translation
%A Yang, Baosong
%A Wong, Derek F.
%A Xiao, Tong
%A Chao, Lidia S.
%A Zhu, Jingbo
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F yang-etal-2017-towards
%X 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.
%R 10.18653/v1/D17-1150
%U https://aclanthology.org/D17-1150
%U https://doi.org/10.18653/v1/D17-1150
%P 1432-1441

Markdown (Informal)

[Towards Bidirectional Hierarchical Representations for Attention-based Neural Machine Translation](https://aclanthology.org/D17-1150) (Yang et al., EMNLP 2017)

• Towards Bidirectional Hierarchical Representations for Attention-based Neural Machine Translation (Yang et al., EMNLP 2017)

ACL

• Baosong Yang, Derek F. Wong, Tong Xiao, Lidia S. Chao, and Jingbo Zhu. 2017. Towards Bidirectional Hierarchical Representations for Attention-based Neural Machine Translation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1432–1441, Copenhagen, Denmark. Association for Computational Linguistics.