Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation

Autor:	Chronopoulou, Alexandra, Stojanovski, Dario, Fraser, Alexander
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Successful methods for unsupervised neural machine translation (UNMT) employ crosslingual 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. Comment: Accepted at NAACL 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.10531 Zobrazit plný text záznamu View this record from Arxiv