@inproceedings{chen-etal-2020-content,
title = "Content Word Aware Neural Machine Translation",
author = "Chen, Kehai and
Wang, Rui and
Utiyama, Masao and
Sumita, Eiichiro",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.34",
doi = "10.18653/v1/2020.acl-main.34",
pages = "358--364",
abstract = "Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-by-word. However, NMT does not consider the importance of word in the sentence meaning, for example, some words (i.e., content words) express more important meaning than others (i.e., function words). To address this limitation, we first utilize word frequency information to distinguish between content and function words in a sentence, and then design a content word-aware NMT to improve translation performance. Empirical results on the WMT14 English-to-German, WMT14 English-to-French, and WMT17 Chinese-to-English translation tasks show that the proposed methods can significantly improve the performance of Transformer-based NMT.",
}
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<abstract>Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-by-word. However, NMT does not consider the importance of word in the sentence meaning, for example, some words (i.e., content words) express more important meaning than others (i.e., function words). To address this limitation, we first utilize word frequency information to distinguish between content and function words in a sentence, and then design a content word-aware NMT to improve translation performance. Empirical results on the WMT14 English-to-German, WMT14 English-to-French, and WMT17 Chinese-to-English translation tasks show that the proposed methods can significantly improve the performance of Transformer-based NMT.</abstract>
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%0 Conference Proceedings
%T Content Word Aware Neural Machine Translation
%A Chen, Kehai
%A Wang, Rui
%A Utiyama, Masao
%A Sumita, Eiichiro
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-content
%X Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-by-word. However, NMT does not consider the importance of word in the sentence meaning, for example, some words (i.e., content words) express more important meaning than others (i.e., function words). To address this limitation, we first utilize word frequency information to distinguish between content and function words in a sentence, and then design a content word-aware NMT to improve translation performance. Empirical results on the WMT14 English-to-German, WMT14 English-to-French, and WMT17 Chinese-to-English translation tasks show that the proposed methods can significantly improve the performance of Transformer-based NMT.
%R 10.18653/v1/2020.acl-main.34
%U https://aclanthology.org/2020.acl-main.34
%U https://doi.org/10.18653/v1/2020.acl-main.34
%P 358-364
Markdown (Informal)
[Content Word Aware Neural Machine Translation](https://aclanthology.org/2020.acl-main.34) (Chen et al., ACL 2020)
ACL
- Kehai Chen, Rui Wang, Masao Utiyama, and Eiichiro Sumita. 2020. Content Word Aware Neural Machine Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 358–364, Online. Association for Computational Linguistics.