@inproceedings{zheng-etal-2019-dynamic,
title = "Dynamic Past and Future for Neural Machine Translation",
author = "Zheng, Zaixiang and
Huang, Shujian and
Tu, Zhaopeng and
Dai, Xin-Yu and
Chen, Jiajun",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1086",
doi = "10.18653/v1/D19-1086",
pages = "931--941",
abstract = "Previous studies have shown that neural machine translation (NMT) models can benefit from explicitly modeling translated () and untranslated () source contents as recurrent states (CITATION). However, this less interpretable recurrent process hinders its power to model the dynamic updating of and contents during decoding. In this paper, we propose to model the \textit{dynamic principles} by explicitly separating source words into groups of translated and untranslated contents through parts-to-wholes assignment. The assignment is learned through a novel variant of routing-by-agreement mechanism (CITATION), namely \textit{Guided Dynamic Routing}, where the translating status at each decoding step \textit{guides} the routing process to assign each source word to its associated group (i.e., translated or untranslated content) represented by a capsule, enabling translation to be made from holistic context. Experiments show that our approach achieves substantial improvements over both Rnmt and Transformer by producing more adequate translations. Extensive analysis demonstrates that our method is highly interpretable, which is able to recognize the translated and untranslated contents as expected.",
}
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<abstract>Previous studies have shown that neural machine translation (NMT) models can benefit from explicitly modeling translated () and untranslated () source contents as recurrent states (CITATION). However, this less interpretable recurrent process hinders its power to model the dynamic updating of and contents during decoding. In this paper, we propose to model the dynamic principles by explicitly separating source words into groups of translated and untranslated contents through parts-to-wholes assignment. The assignment is learned through a novel variant of routing-by-agreement mechanism (CITATION), namely Guided Dynamic Routing, where the translating status at each decoding step guides the routing process to assign each source word to its associated group (i.e., translated or untranslated content) represented by a capsule, enabling translation to be made from holistic context. Experiments show that our approach achieves substantial improvements over both Rnmt and Transformer by producing more adequate translations. Extensive analysis demonstrates that our method is highly interpretable, which is able to recognize the translated and untranslated contents as expected.</abstract>
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%0 Conference Proceedings
%T Dynamic Past and Future for Neural Machine Translation
%A Zheng, Zaixiang
%A Huang, Shujian
%A Tu, Zhaopeng
%A Dai, Xin-Yu
%A Chen, Jiajun
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F zheng-etal-2019-dynamic
%X Previous studies have shown that neural machine translation (NMT) models can benefit from explicitly modeling translated () and untranslated () source contents as recurrent states (CITATION). However, this less interpretable recurrent process hinders its power to model the dynamic updating of and contents during decoding. In this paper, we propose to model the dynamic principles by explicitly separating source words into groups of translated and untranslated contents through parts-to-wholes assignment. The assignment is learned through a novel variant of routing-by-agreement mechanism (CITATION), namely Guided Dynamic Routing, where the translating status at each decoding step guides the routing process to assign each source word to its associated group (i.e., translated or untranslated content) represented by a capsule, enabling translation to be made from holistic context. Experiments show that our approach achieves substantial improvements over both Rnmt and Transformer by producing more adequate translations. Extensive analysis demonstrates that our method is highly interpretable, which is able to recognize the translated and untranslated contents as expected.
%R 10.18653/v1/D19-1086
%U https://aclanthology.org/D19-1086
%U https://doi.org/10.18653/v1/D19-1086
%P 931-941
Markdown (Informal)
[Dynamic Past and Future for Neural Machine Translation](https://aclanthology.org/D19-1086) (Zheng et al., EMNLP-IJCNLP 2019)
ACL
- Zaixiang Zheng, Shujian Huang, Zhaopeng Tu, Xin-Yu Dai, and Jiajun Chen. 2019. Dynamic Past and Future for Neural Machine Translation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 931–941, Hong Kong, China. Association for Computational Linguistics.