  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.
