论文标题
分析和模拟对会话推荐系统中的用户话语重新调整
Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems
论文作者
论文摘要
用户模拟是一种用于评估对话推荐系统的经济高效技术。但是，构建类似人类的模拟器仍然是一个悬而未决的挑战。在这项工作中，我们专注于用户如何在对话代理无法理解它们时如何重新制定话语。首先，我们进行了一项用户研究，涉及跨不同领域的五个对话代理，以确定共同的重新重新制定类型及其过渡关系。出现的一种常见模式是，持续的用户将首先尝试在放弃之前重新尝试，然后简化。接下来，为了将观察到的重新制作行为纳入用户模拟器中，我们介绍了重新制定序列生成的任务：以给定的意图生成一系列重新计算的话语（重新形式或简化）。我们通过扩展以重新制定类型为指导的变压器模型来开发方法，并根据估计的阅读难度执行进一步的过滤。我们使用自动和人类评估证明了方法的有效性。
User simulation has been a cost-effective technique for evaluating conversational recommender systems. However, building a human-like simulator is still an open challenge. In this work, we focus on how users reformulate their utterances when a conversational agent fails to understand them. First, we perform a user study, involving five conversational agents across different domains, to identify common reformulation types and their transition relationships. A common pattern that emerges is that persistent users would first try to rephrase, then simplify, before giving up. Next, to incorporate the observed reformulation behavior in a user simulator, we introduce the task of reformulation sequence generation: to generate a sequence of reformulated utterances with a given intent (rephrase or simplify). We develop methods by extending transformer models guided by the reformulation type and perform further filtering based on estimated reading difficulty. We demonstrate the effectiveness of our approach using both automatic and human evaluation.
