@inproceedings{zhang-etal-2021-point,
title = "Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation",
author = "Zhang, Tong and
Zhang, Long and
Ye, Wei and
Li, Bo and
Sun, Jinan and
Zhu, Xiaoyu and
Zhao, Wen and
Zhang, Shikun",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.307",
doi = "10.18653/v1/2021.acl-long.307",
pages = "3970--3979",
abstract = "This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models. By introducing three novel components: Pointer, Disambiguator, and Copier, our method PDC achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionaries can potentially be used; (2) Disambiguator synthesizes contextual information from the source view and the target view, both of which contribute to distinguishing the proper translation of a specific source word from multiple candidates in dictionaries; (3) Copier systematically connects Pointer and Disambiguator based on a hierarchical copy mechanism seamlessly integrated with Transformer, thereby building an end-to-end architecture that could avoid error propagation problems in alternative pipe-line methods. The experimental results on Chinese-English and English-Japanese benchmarks demonstrate the PDC{'}s overall superiority and effectiveness of each component.",
}
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<abstract>This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models. By introducing three novel components: Pointer, Disambiguator, and Copier, our method PDC achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionaries can potentially be used; (2) Disambiguator synthesizes contextual information from the source view and the target view, both of which contribute to distinguishing the proper translation of a specific source word from multiple candidates in dictionaries; (3) Copier systematically connects Pointer and Disambiguator based on a hierarchical copy mechanism seamlessly integrated with Transformer, thereby building an end-to-end architecture that could avoid error propagation problems in alternative pipe-line methods. The experimental results on Chinese-English and English-Japanese benchmarks demonstrate the PDC’s overall superiority and effectiveness of each component.</abstract>
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%0 Conference Proceedings
%T Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation
%A Zhang, Tong
%A Zhang, Long
%A Ye, Wei
%A Li, Bo
%A Sun, Jinan
%A Zhu, Xiaoyu
%A Zhao, Wen
%A Zhang, Shikun
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 aug
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2021-point
%X This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models. By introducing three novel components: Pointer, Disambiguator, and Copier, our method PDC achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionaries can potentially be used; (2) Disambiguator synthesizes contextual information from the source view and the target view, both of which contribute to distinguishing the proper translation of a specific source word from multiple candidates in dictionaries; (3) Copier systematically connects Pointer and Disambiguator based on a hierarchical copy mechanism seamlessly integrated with Transformer, thereby building an end-to-end architecture that could avoid error propagation problems in alternative pipe-line methods. The experimental results on Chinese-English and English-Japanese benchmarks demonstrate the PDC’s overall superiority and effectiveness of each component.
%R 10.18653/v1/2021.acl-long.307
%U https://aclanthology.org/2021.acl-long.307
%U https://doi.org/10.18653/v1/2021.acl-long.307
%P 3970-3979
Markdown (Informal)
[Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation](https://aclanthology.org/2021.acl-long.307) (Zhang et al., ACL 2021)
ACL
- Tong Zhang, Long Zhang, Wei Ye, Bo Li, Jinan Sun, Xiaoyu Zhu, Wen Zhao, and Shikun Zhang. 2021. Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3970–3979, Online. Association for Computational Linguistics.