Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework

Minghao Zhu, Junli Wang, Chungang Yan


Abstract
Variational Autoencoder (VAE) is an effective framework to model the interdependency for non-autoregressive neural machine translation (NAT). One of the prominent VAE-based NAT frameworks, LaNMT, achieves great improvements to vanilla models, but still suffers from two main issues which lower down the translation quality: (1) mismatch between training and inference circumstances and (2) inadequacy of latent representations. In this work, we target on addressing these issues by proposing posterior consistency regularization. Specifically, we first perform stochastic data augmentation on the input samples to better adapt the model for inference circumstance, and then conduct consistency training on posterior latent variables to construct a more robust latent representations without any expansion on latent size. Experiments on En<->De and En<->Ro benchmarks confirm the effectiveness of our methods with about 1.5/0.7 and 0.8/0.3 BLEU points improvement to the baseline model with about 12.6× faster than autoregressive Transformer.
Anthology ID:
2022.naacl-main.45
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
607–617
Language:
URL:
https://aclanthology.org/2022.naacl-main.45
DOI:
10.18653/v1/2022.naacl-main.45
Bibkey:
Cite (ACL):
Minghao Zhu, Junli Wang, and Chungang Yan. 2022. Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 607–617, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework (Zhu et al., NAACL 2022)
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PDF:
https://aclanthology.org/2022.naacl-main.45.pdf