Explicit Cross-lingual Pre-training for Unsupervised Machine Translation

Shuo Ren, Yu Wu, Shujie Liu, Ming Zhou, Shuai Ma

Abstract
Pre-training has proven to be effective in unsupervised machine translation due to its ability to model deep context information in cross-lingual scenarios. However, the cross-lingual information obtained from shared BPE spaces is inexplicit and limited. In this paper, we propose a novel cross-lingual pre-training method for unsupervised machine translation by incorporating explicit cross-lingual training signals. Specifically, we first calculate cross-lingual n-gram embeddings and infer an n-gram translation table from them. With those n-gram translation pairs, we propose a new pre-training model called Cross-lingual Masked Language Model (CMLM), which randomly chooses source n-grams in the input text stream and predicts their translation candidates at each time step. Experiments show that our method can incorporate beneficial cross-lingual information into pre-trained models. Taking pre-trained CMLM models as the encoder and decoder, we significantly improve the performance of unsupervised machine translation.
Anthology ID:
D19-1071
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
770–779
Language:
URL:
https://aclanthology.org/D19-1071
DOI:
10.18653/v1/D19-1071
Bibkey:
Cite (ACL):
Shuo Ren, Yu Wu, Shujie Liu, Ming Zhou, and Shuai Ma. 2019. Explicit Cross-lingual Pre-training for Unsupervised Machine Translation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 770–779, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Explicit Cross-lingual Pre-training for Unsupervised Machine Translation (Ren et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/D19-1071.pdf
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