Quality Estimation-Assisted Automatic Post-Editing
Deoghare,Sourabh Kanojia,Diptesh Ranasinghe,Tharindu Blain,Frédéric Bhattacharyya,Pushpak
Deoghare,Sourabh
Kanojia,Diptesh
Ranasinghe,Tharindu
Blain,Frédéric
Bhattacharyya,Pushpak
Abstract
Automatic Post-Editing (APE) systems are prone to over-correction of the Machine Translation (MT) outputs. While a Word-level Quality Estimation (QE) system can provide a way to curtail the over-correction, a significant performance gain has not been observed thus far by utilizing existing APE and QE combination strategies. This paper proposes joint training of a model over QE (sentence- and word-level) and APE tasks to improve the APE. Our proposed approach utilizes a multi-task learning (MTL) methodology, which shows significant improvement while treating the tasks as a 'bargaining game' during training. Moreover, we investigate various existing combination strategies and show that our approach achieves state-of-the-art performance for a 'distant' language pair, viz., English-Marathi. We observe an improvement of 1.09 TER and 1.37 BLEU points over a baseline QE-Unassisted APE system for English-Marathi while also observing 0.46 TER and 0.62 BLEU points improvement for English-German. Further, we discuss the results qualitatively and show how our approach helps reduce over-correction, thereby improving the APE performance. We also observe that the degree of integration between QE and APE directly correlates with the APE performance gain. We release our code publicly.
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Publisher Copyright: © 2023 Association for Computational Linguistics.
Date
2023
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Association for Computational Linguistics (ACL)
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Citation
Deoghare, S, Kanojia, D, Ranasinghe, T, Blain, F & Bhattacharyya, P 2023, Quality Estimation-Assisted Automatic Post-Editing. in Findings of the Association for Computational Linguistics : EMNLP 2023. Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics (ACL), pp. 1686-1698, 2023 Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, Singapore, 6/12/23. https://doi.org/0.18653/v1/2023.findings-emnlp.115