EMNLP 2021finding2 citations
Secoco: Self-Correcting Encoding for Neural Machine Translation
Tao Wang, Chengqi Zhao, Mingxuan Wang, Lei Li, Hang Li, Deyi Xiong
Abstract
This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with noisy input for robust neural machine translation by introducing self-correcting predictors. Different from previous robust approaches, Secoco enables NMT to explicitly correct noisy inputs and delete specific errors simultaneously with the translation decoding process. Secoco is able to achieve significant improvements over strong baselines on two real-world test sets and a benchmark WMT dataset with good interpretability. We will make our code and dataset publicly available soon.
BibTeX
@inproceedings{wang-etal-2021-secoco-self,
title = "Secoco: Self-Correcting Encoding for Neural Machine Translation",
author = "Wang, Tao and
Zhao, Chengqi and
Wang, Mingxuan and
Li, Lei and
Li, Hang and
Xiong, Deyi",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.396/",
doi = "10.18653/v1/2021.findings-emnlp.396",
pages = "4639--4644"
}