论文标题
Consai3：为开放域对话系统（CLARIQ）生成澄清问题
ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)
论文作者
论文摘要
本文档详细介绍了有关澄清对话系统问题（CLARIQ）的挑战。挑战是在2020年在面向搜索的对话AI（SCAI）EMNLP研讨会上的对话AI挑战系列（Consai3）的一部分组织的。对话系统的主要目的是返回适当的答案，以响应用户请求。但是，某些用户请求可能模棱两可。在红外设置中，这种情况被处理主要认为搜索结果页面的多元化。但是，在有限的带宽中，在对话环境中，它更具挑战性。因此，在这一挑战中，我们提供了一个共同的评估框架来评估混合启动性对话。要求参与者在寻求信息的对话中对澄清问题进行排名。挑战是在两个阶段组织的，在阶段1中，我们在离线环境和单转交谈中评估提交的内容。第1阶段的顶级参与者有机会通过人类注释者对其模型进行测试。
This document presents a detailed description of the challenge on clarifying questions for dialogue systems (ClariQ). The challenge is organized as part of the Conversational AI challenge series (ConvAI3) at Search Oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In IR settings such a situation is handled mainly thought the diversification of the search result page. It is however much more challenging in dialogue settings with limited bandwidth. Therefore, in this challenge, we provide a common evaluation framework to evaluate mixed-initiative conversations. Participants are asked to rank clarifying questions in an information-seeking conversations. The challenge is organized in two stages where in Stage 1 we evaluate the submissions in an offline setting and single-turn conversations. Top participants of Stage 1 get the chance to have their model tested by human annotators.
