  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.
