论文标题
我应该问什么：一种知识驱动的方法，用于对话调查中的后续问题产生
What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys
论文作者
论文摘要
通过启用更具动态和个性化的调查结构，可以实现即时产生后续问题可以显着改善对话性调查质量和用户体验。在本文中，我们提出了一项新颖的任务，以在对话调查中进行知识驱动的后续问题产生。我们在对话历史上构建了一个新的人为写的后续问题的人类宣传的数据集，并在对话调查的背景下标记了知识。与数据集一起，我们设计并验证了一组无参考的Gricean启发的评估指标，以系统地评估生成的后续问题的质量。然后，我们为任务提出了一个两阶段的知识驱动模型，该模型通过使用知识来指导生成过程来生成信息丰富且连贯的后续问题。实验表明，与基于GPT的基线模型相比，我们的两级模型产生了更多信息，连贯和清晰的后续问题。
Generating follow-up questions on the fly could significantly improve conversational survey quality and user experiences by enabling a more dynamic and personalized survey structure. In this paper, we proposed a novel task for knowledge-driven follow-up question generation in conversational surveys. We constructed a new human-annotated dataset of human-written follow-up questions with dialogue history and labeled knowledge in the context of conversational surveys. Along with the dataset, we designed and validated a set of reference-free Gricean-inspired evaluation metrics to systematically evaluate the quality of generated follow-up questions. We then propose a two-staged knowledge-driven model for the task, which generates informative and coherent follow-up questions by using knowledge to steer the generation process. The experiments demonstrate that compared to GPT-based baseline models, our two-staged model generates more informative, coherent, and clear follow-up questions.
