Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction
Gyubok Lee, Seongjun Yang, Edward Choi
Abstract
Accurate terminology translation is crucial for ensuring the practicality and reliability of neural machine translation (NMT) systems. To address this, lexically constrained NMT explores various methods to ensure pre-specified words and phrases appear in the translation output. However, in many cases, those methods are studied on general domain corpora, where the terms are mostly uni- and bi-grams (>98%). In this paper, we instead tackle a more challenging setup consisting of domain-specific corpora with much longer n-gram and highly specialized terms. Inspired by the recent success of masked span prediction models, we propose a simple and effective training strategy that achieves consistent improvements on both terminology and sentence-level translation for three domain-specific corpora in two language pairs.
BibTeX
@inproceedings{lee-etal-2021-improving,
title = "Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction",
author = "Lee, Gyubok and
Yang, Seongjun and
Choi, Edward",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.94/",
doi = "10.18653/v1/2021.acl-short.94",
pages = "743--753"
}