利用长文本上下文提升机器翻译质量评估准确率
Long-context Reference-based MT Quality Estimation
- 基于COMET框架，用长段落上下文数据训练模型
- 相比短片段模型，与人工评分相关性提升显著
- 适合需要高精度翻译质量评估的场景
本文介绍我们参加第十届机器翻译会议（WMT25）自动翻译质量评估共享任务的系统。系统基于COMET框架，利用领域内人工标注的长文本段落构建增强数据，通过加权平均计算整体得分，以预测分段级错误段注释（ESA）分数。我们整合了多个人工判断数据集（MQM、SQM、DA），通过归一化量表后训练多语言回归模型，从源句、假设句和参考句中预测质量得分。实验表明，引入长上下文信息后，模型与人工评分的相关性优于仅使用短段落训练的模型。
原文摘要 · Abstract (English)
In this paper, we present our submission to the Tenth Conference on Machine Translation (WMT25) Shared Task on Automated Translation Quality Evaluation. Our systems are built upon the COMET framework and trained to predict segment-level Error Span Annotation (ESA) scores using augmented long-context data. To construct long-context training data, we concatenate in-domain, human-annotated sentences and compute a weighted average of their scores. We integrate multiple human judgment datasets (MQM, SQM, and DA) by normalising their scales and train multilingual regression models to predict quality scores from the source, hypothesis, and reference translations. Experimental results show that incorporating long-context information improves correlations with human judgments compared to models trained only on short segments.
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