XLM-D: Decorate Cross-lingual Pre-training Model as Non-Autoregressive Neural Machine Translation

Yong Wang, Shilin He, Guanhua Chen, Yun Chen, Daxin Jiang

Abstract
Pre-training language models have achieved thriving success in numerous natural language understanding and autoregressive generation tasks, but non-autoregressive generation in applications such as machine translation has not sufficiently benefited from the pre-training paradigm. In this work, we establish the connection between a pre-trained masked language model (MLM) and non-autoregressive generation on machine translation. From this perspective, we present XLM-D, which seamlessly transforms an off-the-shelf cross-lingual pre-training model into a non-autoregressive translation (NAT) model with a lightweight yet effective decorator. Specifically, the decorator ensures the representation consistency of the pre-trained model and brings only one additional trainable parameter. Extensive experiments on typical translation datasets show that our models obtain state-of-the-art performance while realizing the inference speed-up by 19.9x. One striking result is that on WMT14 En-De, our XLM-D obtains 29.80 BLEU points with multiple iterations, which outperforms the previous mask-predict model by 2.77 points.
Anthology ID:
2022.emnlp-main.466
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
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Publisher:
Association for Computational Linguistics
Note:
Pages:
6934–6946
Language:
URL:
https://aclanthology.org/2022.emnlp-main.466
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Cite (ACL):
Yong Wang, Shilin He, Guanhua Chen, Yun Chen, and Daxin Jiang. 2022. XLM-D: Decorate Cross-lingual Pre-training Model as Non-Autoregressive Neural Machine Translation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6934–6946, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
XLM-D: Decorate Cross-lingual Pre-training Model as Non-Autoregressive Neural Machine Translation (Wang et al., EMNLP 2022)
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