@inproceedings{zhong-chiang-2020-look,
title = "Look It Up: Bilingual and Monolingual Dictionaries Improve Neural Machine Translation",
author = "Zhong, Xing Jie and
Chiang, David",
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.65",
pages = "538--549",
abstract = "Despite advances in neural machine translation (NMT) quality, rare words continue to be problematic. For humans, the solution to the rare-word problem has long been dictionaries, but dictionaries cannot be straightforwardly incorporated into NMT. In this paper, we describe a new method for {``}attaching{''} dictionary definitions to rare words so that the network can learn the best way to use them. We demonstrate improvements of up to 3.1 BLEU using bilingual dictionaries and up to 0.7 BLEU using monolingual source-language dictionaries.",
}
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<abstract>Despite advances in neural machine translation (NMT) quality, rare words continue to be problematic. For humans, the solution to the rare-word problem has long been dictionaries, but dictionaries cannot be straightforwardly incorporated into NMT. In this paper, we describe a new method for “attaching” dictionary definitions to rare words so that the network can learn the best way to use them. We demonstrate improvements of up to 3.1 BLEU using bilingual dictionaries and up to 0.7 BLEU using monolingual source-language dictionaries.</abstract>
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%0 Conference Proceedings
%T Look It Up: Bilingual and Monolingual Dictionaries Improve Neural Machine Translation
%A Zhong, Xing Jie
%A Chiang, David
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F zhong-chiang-2020-look
%X Despite advances in neural machine translation (NMT) quality, rare words continue to be problematic. For humans, the solution to the rare-word problem has long been dictionaries, but dictionaries cannot be straightforwardly incorporated into NMT. In this paper, we describe a new method for “attaching” dictionary definitions to rare words so that the network can learn the best way to use them. We demonstrate improvements of up to 3.1 BLEU using bilingual dictionaries and up to 0.7 BLEU using monolingual source-language dictionaries.
%U https://aclanthology.org/2020.wmt-1.65
%P 538-549
Markdown (Informal)
[Look It Up: Bilingual and Monolingual Dictionaries Improve Neural Machine Translation](https://aclanthology.org/2020.wmt-1.65) (Zhong & Chiang, WMT 2020)
ACL