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arXiv · 2208.02578

N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models

Shiki Sato·Reina Akama·Hiroki Ouchi·Ryoko Tokuhisa·Jun Suzuki·Kentaro Inui
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Abstract

Avoiding the generation of responses that contradict the preceding context is a significant challenge in dialogue response generation. One feasible method is post-processing, such as filtering out contradicting responses from a resulting n-best response list. In this scenario, the quality of the n-best list considerably affects the occurrence of contradictions because the final response is chosen from this n-best list. This study quantitatively analyzes the contextual contradiction-awareness of neural response generation models using the consistency of the n-best lists. Particularly, we used polar questions as stimulus inputs for concise and quantitative analyses. Our tests illustrate the contradiction-awareness of recent neural response generation models and methodologies, followed by a discussion of their properties and limitations.

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Shiki Sato, Reina Akama, Hiroki Ouchi, Ryoko Tokuhisa, Jun Suzuki, Kentaro Inui. 2022-08-04. N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models. https://arxiv.org/abs/2208.02578

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