论文标题
基于n-最大响应的神经反应产生模型中矛盾意识的分析
N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models
论文作者
论文摘要
避免产生与先前环境相矛盾的响应是对话响应产生的重大挑战。一种可行的方法是后处理，例如从最终的n-tesp响应列表中滤除矛盾的响应。在这种情况下，n最佳列表的质量极大地影响了矛盾的发生，因为最终响应是从这个N最佳列表中选择的。这项研究使用n-最佳列表的一致性定量地分析了神经反应产生模型的上下文矛盾意识。特别是，我们使用极性问题作为简洁和定量分析的刺激输入。我们的测试说明了最近的神经反应产生模型和方法的矛盾意识，然后讨论了它们的性质和局限性。
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.
