@inproceedings{ishiwatari-etal-2017-chunk,
title = "Chunk-based Decoder for Neural Machine Translation",
author = "Ishiwatari, Shonosuke and
Yao, Jingtao and
Liu, Shujie and
Li, Mu and
Zhou, Ming and
Yoshinaga, Naoki and
Kitsuregawa, Masaru and
Jia, Weijia",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1174",
doi = "10.18653/v1/P17-1174",
pages = "1901--1912",
abstract = "Chunks (or phrases) once played a pivotal role in machine translation. By using a chunk rather than a word as the basic translation unit, local (intra-chunk) and global (inter-chunk) word orders and dependencies can be easily modeled. The chunk structure, despite its importance, has not been considered in the decoders used for neural machine translation (NMT). In this paper, we propose chunk-based decoders for (NMT), each of which consists of a chunk-level decoder and a word-level decoder. The chunk-level decoder models global dependencies while the word-level decoder decides the local word order in a chunk. To output a target sentence, the chunk-level decoder generates a chunk representation containing global information, which the word-level decoder then uses as a basis to predict the words inside the chunk. Experimental results show that our proposed decoders can significantly improve translation performance in a WAT {`}16 English-to-Japanese translation task.",
}
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<abstract>Chunks (or phrases) once played a pivotal role in machine translation. By using a chunk rather than a word as the basic translation unit, local (intra-chunk) and global (inter-chunk) word orders and dependencies can be easily modeled. The chunk structure, despite its importance, has not been considered in the decoders used for neural machine translation (NMT). In this paper, we propose chunk-based decoders for (NMT), each of which consists of a chunk-level decoder and a word-level decoder. The chunk-level decoder models global dependencies while the word-level decoder decides the local word order in a chunk. To output a target sentence, the chunk-level decoder generates a chunk representation containing global information, which the word-level decoder then uses as a basis to predict the words inside the chunk. Experimental results show that our proposed decoders can significantly improve translation performance in a WAT ‘16 English-to-Japanese translation task.</abstract>
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%0 Conference Proceedings
%T Chunk-based Decoder for Neural Machine Translation
%A Ishiwatari, Shonosuke
%A Yao, Jingtao
%A Liu, Shujie
%A Li, Mu
%A Zhou, Ming
%A Yoshinaga, Naoki
%A Kitsuregawa, Masaru
%A Jia, Weijia
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F ishiwatari-etal-2017-chunk
%X Chunks (or phrases) once played a pivotal role in machine translation. By using a chunk rather than a word as the basic translation unit, local (intra-chunk) and global (inter-chunk) word orders and dependencies can be easily modeled. The chunk structure, despite its importance, has not been considered in the decoders used for neural machine translation (NMT). In this paper, we propose chunk-based decoders for (NMT), each of which consists of a chunk-level decoder and a word-level decoder. The chunk-level decoder models global dependencies while the word-level decoder decides the local word order in a chunk. To output a target sentence, the chunk-level decoder generates a chunk representation containing global information, which the word-level decoder then uses as a basis to predict the words inside the chunk. Experimental results show that our proposed decoders can significantly improve translation performance in a WAT ‘16 English-to-Japanese translation task.
%R 10.18653/v1/P17-1174
%U https://aclanthology.org/P17-1174
%U https://doi.org/10.18653/v1/P17-1174
%P 1901-1912
Markdown (Informal)
[Chunk-based Decoder for Neural Machine Translation](https://aclanthology.org/P17-1174) (Ishiwatari et al., ACL 2017)
ACL
- Shonosuke Ishiwatari, Jingtao Yao, Shujie Liu, Mu Li, Ming Zhou, Naoki Yoshinaga, Masaru Kitsuregawa, and Weijia Jia. 2017. Chunk-based Decoder for Neural Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1901–1912, Vancouver, Canada. Association for Computational Linguistics.