Towards Making the Most of Pre-trained Translation Model for Quality Estimation

Li Chunyou, Di Hui, Huang Hui, Ouchi Kazushige, Chen Yufeng, Liu Jian, Xu Jinan


Abstract
“Machine translation quality estimation (QE) aims to evaluate the quality of machine translation automatically without relying on any reference. One common practice is applying the translation model as a feature extractor. However, there exist several discrepancies between the translation model and the QE model. The translation model is trained in an autoregressive manner, while the QE model is performed in a non-autoregressive manner. Besides, the translation model only learns to model human-crafted parallel data, while the QE model needs to model machinetranslated noisy data. In order to bridge these discrepancies, we propose two strategies to posttrain the translation model, namely Conditional Masked Language Modeling (CMLM) and Denoising Restoration (DR). Specifically, CMLM learns to predict masked tokens at the target side conditioned on the source sentence. DR firstly introduces noise to the target side of parallel data, and the model is trained to detect and recover the introduced noise. Both strategies can adapt the pre-trained translation model to the QE-style prediction task. Experimental results show that our model achieves impressive results, significantly outperforming the baseline model, verifying the effectiveness of our proposed methods.”
Anthology ID:
2022.ccl-1.77
Volume:
Proceedings of the 21st Chinese National Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Nanchang, China
Editors:
Maosong Sun (孙茂松), Yang Liu (刘洋), Wanxiang Che (车万翔), Yang Feng (冯洋), Xipeng Qiu (邱锡鹏), Gaoqi Rao (饶高琦), Yubo Chen (陈玉博)
Venue:
CCL
SIG:
Publisher:
Chinese Information Processing Society of China
Note:
Pages:
872–883
Language:
English
URL:
https://aclanthology.org/2022.ccl-1.77
DOI:
Bibkey:
Cite (ACL):
Li Chunyou, Di Hui, Huang Hui, Ouchi Kazushige, Chen Yufeng, Liu Jian, and Xu Jinan. 2022. Towards Making the Most of Pre-trained Translation Model for Quality Estimation. In Proceedings of the 21st Chinese National Conference on Computational Linguistics, pages 872–883, Nanchang, China. Chinese Information Processing Society of China.
Cite (Informal):
Towards Making the Most of Pre-trained Translation Model for Quality Estimation (Chunyou et al., CCL 2022)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/2022.ccl-1.77.pdf