Uncertainty-Aware Semantic Augmentation for Neural Machine Translation

Xiangpeng Wei, Heng Yu, Yue Hu, Rongxiang Weng, Luxi Xing, Weihua Luo


Abstract
As a sequence-to-sequence generation task, neural machine translation (NMT) naturally contains intrinsic uncertainty, where a single sentence in one language has multiple valid counterparts in the other. However, the dominant methods for NMT only observe one of them from the parallel corpora for the model training but have to deal with adequate variations under the same meaning at inference. This leads to a discrepancy of the data distribution between the training and the inference phases. To address this problem, we propose uncertainty-aware semantic augmentation, which explicitly captures the universal semantic information among multiple semantically-equivalent source sentences and enhances the hidden representations with this information for better translations. Extensive experiments on various translation tasks reveal that our approach significantly outperforms the strong baselines and the existing methods.
Anthology ID:
2020.emnlp-main.216
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2724–2735
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.216
DOI:
10.18653/v1/2020.emnlp-main.216
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.216.pdf
Optional supplementary material:
 2020.emnlp-main.216.OptionalSupplementaryMaterial.zip