Changing speaker names consistently throughout a dialogue should not affect its meaning and corresponding outputs for text generation from dialogues. However, pre-trained language models, serving as the backbone for dialogue-processing tasks, have shown to be sensitive to nuances. This may result in unfairness in real-world applications. No comprehensive analysis of this problem has been done in the past. In this work, we propose to quantitatively measure a model's sensitivity on speaker names, and comprehensively evaluate a number of known methods for reducing speaker name sensitivity, including a novel approach of our own. Extensive experiments on multiple datasets provide a benchmark for this problem and show the favorable performance of our approach in sensitivity reduction and quality of generation.

翻译：在对话中一致性地更改说话人名称不应影响其含义及对话文本生成的对应输出。然而，作为对话处理任务核心的预训练语言模型已被证明对细微差异敏感，这可能导致实际应用中的不公平性。过去对此问题缺乏全面的分析。本研究提出通过定量方法衡量模型对说话人名称的敏感度，系统性地评估了多种现有降低说话人名称敏感度的技术，并提出了首创性方法。跨多个数据集的广泛实验为本问题建立了评测基准，同时验证了我们方法在敏感度降低与生成质量方面的优越性能。