NAACL 2021long5 citations
Smart-Start Decoding for Neural Machine Translation
Jian Yang, Shuming Ma, Dongdong Zhang, Juncheng Wan, Zhoujun Li, Ming Zhou
Abstract
Most current neural machine translation models adopt a monotonic decoding order of either left-to-right or right-to-left. In this work, we propose a novel method that breaks up the limitation of these decoding orders, called Smart-Start decoding. More specifically, our method first predicts a median word. It starts to decode the words on the right side of the median word and then generates words on the left. We evaluate the proposed Smart-Start decoding method on three datasets. Experimental results show that the proposed method can significantly outperform strong baseline models.
BibTeX
@inproceedings{yang-etal-2021-smart,
title = "Smart-Start Decoding for Neural Machine Translation",
author = "Yang, Jian and
Ma, Shuming and
Zhang, Dongdong and
Wan, Juncheng and
Li, Zhoujun and
Zhou, Ming",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.312/",
doi = "10.18653/v1/2021.naacl-main.312",
pages = "3982--3988"
}