Cross-lingual Retrieval for Iterative Self-Supervised Training

• URL: http://arxiv.org/abs/2006.09526v2
• Date: Mon, 26 Oct 2020 23:25:31 GMT
• Title: Cross-lingual Retrieval for Iterative Self-Supervised Training
• Authors: Chau Tran, Yuqing Tang, Xian Li, Jiatao Gu
• Abstract summary: Cross-lingual alignment can be further improved by training seq2seq models on sentence pairs mined using their own encoder outputs. We develop a new approach -- cross-lingual retrieval for iterative self-supervised training.
• Score: 66.3329263451598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies have demonstrated the cross-lingual alignment ability of multilingual pretrained language models. In this work, we found that the cross-lingual alignment can be further improved by training seq2seq models on sentence pairs mined using their own encoder outputs. We utilized these findings to develop a new approach -- cross-lingual retrieval for iterative self-supervised training (CRISS), where mining and training processes are applied iteratively, improving cross-lingual alignment and translation ability at the same time. Using this method, we achieved state-of-the-art unsupervised machine translation results on 9 language directions with an average improvement of 2.4 BLEU, and on the Tatoeba sentence retrieval task in the XTREME benchmark on 16 languages with an average improvement of 21.5% in absolute accuracy. Furthermore, CRISS also brings an additional 1.8 BLEU improvement on average compared to mBART, when finetuned on supervised machine translation downstream tasks.

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