Title: Long-context Reference-based MT Quality Estimation Show Chinese title

Title: 基于长上下文引用的机器翻译质量评估

Abstract: 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. Show Chinese abstract

Abstract: 本文中，我们介绍了提交给第十届机器翻译会议（WMT25）自动化翻译质量评估共享任务的系统。我们的系统基于COMET框架，并利用增强的长上下文数据训练以预测段级错误跨度注释（ESA）分数。为了构建长上下文训练数据，我们连接领域内的人工标注句子并计算它们的分数加权平均值。我们通过规范化其尺度来整合多个人工判断数据集（MQM、SQM和DA），并训练多语言回归模型从源语言、假设和参考翻译中预测质量分数。实验结果表明，与仅在短段落上训练的模型相比，结合长上下文信息能提高与人工判断的相关性。