Visual Cues and Error Correction for Translation Robustness
Zhenhao Li, Marek Rei, Lucia Specia
Abstract
Neural Machine Translation models are sensitive to noise in the input texts, such as misspelled words and ungrammatical constructions. Existing robustness techniques generally fail when faced with unseen types of noise and their performance degrades on clean texts. In this paper, we focus on three types of realistic noise that are commonly generated by humans and introduce the idea of visual context to improve translation robustness for noisy texts. In addition, we describe a novel error correction training regime that can be used as an auxiliary task to further improve translation robustness. Experiments on English-French and English-German translation show that both multimodal and error correction components improve model robustness to noisy texts, while still retaining translation quality on clean texts.
BibTeX
@inproceedings{li-etal-2021-visual-cues,
title = "Visual Cues and Error Correction for Translation Robustness",
author = "Li, Zhenhao and
Rei, Marek and
Specia, Lucia",
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.271/",
doi = "10.18653/v1/2021.findings-emnlp.271",
pages = "3153--3168"
}