Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation
Alexandra Chronopoulou, Dario Stojanovski, Alexander Fraser
Abstract
Successful methods for unsupervised neural machine translation (UNMT) employ cross-lingual pretraining via self-supervision, often in the form of a masked language modeling or a sequence generation task, which requires the model to align the lexical- and high-level representations of the two languages. While cross-lingual pretraining works for similar languages with abundant corpora, it performs poorly in low-resource and distant languages. Previous research has shown that this is because the representations are not sufficiently aligned. In this paper, we enhance the bilingual masked language model pretraining with lexical-level information by using type-level cross-lingual subword embeddings. Empirical results demonstrate improved performance both on UNMT (up to 4.5 BLEU) and bilingual lexicon induction using our method compared to a UNMT baseline.
BibTeX
@inproceedings{chronopoulou-etal-2021-improving,
title = "Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation",
author = "Chronopoulou, Alexandra and
Stojanovski, Dario and
Fraser, Alexander",
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.16/",
doi = "10.18653/v1/2021.naacl-main.16",
pages = "173--180"
}