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arXiv · 2305.13833

Reducing Sensitivity on Speaker Names for Text Generation from Dialogues

Qi Jia·Haifeng Tang·Kenny Q. Zhu
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Abstract

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.

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Qi Jia, Haifeng Tang, Kenny Q. Zhu. 2023-05-23. Reducing Sensitivity on Speaker Names for Text Generation from Dialogues. https://arxiv.org/abs/2305.13833

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