Alleviating Distribution Shift in Synthetic Data for Machine Translation Quality Estimation

• URL: http://arxiv.org/abs/2502.19941v2
• Date: Fri, 28 Feb 2025 09:51:26 GMT
• Title: Alleviating Distribution Shift in Synthetic Data for Machine Translation Quality Estimation
• Authors: Xiang Geng, Zhejian Lai, Jiajun Chen, Hao Yang, Shujian Huang,
• Abstract summary: We introduce ADSQE, a novel framework for alleviating distribution shift in synthetic QE data.<n>ADSQE uses references, i.e., translation supervision signals, to guide both the generation and annotation processes.<n>Experiments demonstrate that ADSQE outperforms SOTA baselines like COMET in both supervised and unsupervised settings.
• Score: 55.73341401764367
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Quality Estimation (QE) models evaluate the quality of machine translations without reference translations, serving as the reward models for the translation task. Due to the data scarcity, synthetic data generation has emerged as a promising solution. However, synthetic QE data often suffers from distribution shift, which can manifest as discrepancies between pseudo and real translations, or in pseudo labels that do not align with human preferences. To tackle this issue, we introduce ADSQE, a novel framework for alleviating distribution shift in synthetic QE data. To reduce the difference between pseudo and real translations, we employ the constrained beam search algorithm and enhance translation diversity through the use of distinct generation models. ADSQE uses references, i.e., translation supervision signals, to guide both the generation and annotation processes, enhancing the quality of word-level labels. ADSE further identifies the shortest phrase covering consecutive error tokens, mimicking human annotation behavior, to assign the final phrase-level labels. Specially, we underscore that the translation model can not annotate translations of itself accurately. Extensive experiments demonstrate that ADSQE outperforms SOTA baselines like COMET in both supervised and unsupervised settings. Further analysis offers insights into synthetic data generation that could benefit reward models for other tasks.

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