N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models
This addresses the challenge of avoiding contradictory responses in dialogue systems, but it is incremental as it focuses on analysis rather than proposing a new solution.
The study tackled the problem of neural dialogue models generating responses that contradict the context by analyzing contradiction-awareness using n-best response lists, finding that the quality of these lists significantly affects contradiction occurrence.
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.