Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages

Yunsu Kim, Petre Petrov, Pavel Petrushkov, Shahram Khadivi, Hermann Ney

Abstract
We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i.e., source-pivot and pivot-target, leading to a significant improvement in source-target translation. We propose three methods to increase the relation among source, pivot, and target languages in the pre-training: 1) step-wise training of a single model for different language pairs, 2) additional adapter component to smoothly connect pre-trained encoder and decoder, and 3) cross-lingual encoder training via autoencoding of the pivot language. Our methods greatly outperform multilingual models up to +2.6% BLEU in WMT 2019 French-German and German-Czech tasks. We show that our improvements are valid also in zero-shot/zero-resource scenarios.
Anthology ID:
D19-1080
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
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
866–876
Language:
URL:
https://preview.aclanthology.org/icon-24-ingestion/D19-1080/
DOI:
10.18653/v1/D19-1080
Bibkey:
Cite (ACL):
Yunsu Kim, Petre Petrov, Pavel Petrushkov, Shahram Khadivi, and Hermann Ney. 2019. Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages. 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 866–876, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages (Kim et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/icon-24-ingestion/D19-1080.pdf
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