Document-level translation models are usually evaluated using general metrics such as BLEU, which are not informative about the benefits of context. Current work on context-aware evaluation, such as contrastive methods, only measure translation accuracy on words that need context for disambiguation. Such measures cannot reveal whether the translation model uses the correct supporting context. We propose to complement accuracy-based evaluation with measures of context utilization. We find that perturbation-based analysis (comparing models' performance when provided with correct versus random context) is an effective measure of overall context utilization. For a finer-grained phenomenon-specific evaluation, we propose to measure how much the supporting context contributes to handling context-dependent discourse phenomena. We show that automatically-annotated supporting context gives similar conclusions to human-annotated context and can be used as alternative for cases where human annotations are not available. Finally, we highlight the importance of using discourse-rich datasets when assessing context utilization.

翻译：文档级翻译模型通常使用通用指标（如BLEU）进行评估，但这些指标无法有效反映上下文带来的效益。现有上下文感知评估方法（如对比法）仅能衡量需要上下文消歧的词语的翻译准确性，无法揭示模型是否使用了正确的支持性上下文。我们提出在基于准确率的评估基础上，补充上下文利用度的度量方法。研究发现：基于扰动的分析（比较模型在正确上下文与随机上下文下的表现差异）是衡量整体上下文利用度的有效手段。针对更细粒度的现现象级评估，我们提出度量支持性上下文对处理上下文依赖的话语现象的贡献程度。实验表明，自动标注的支持性上下文与人工标注的上下文可得出相似结论，能在缺乏人工标注的情况下作为替代方案。最后，我们强调在评估上下文利用度时应当使用富含话语信息的语料集。