通过检索具有区分性的澄清问题解决意图歧义
Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questions
AI总结：
针对任务导向对话中用户查询在两个意图间存在歧义且领域澄清问题获取成本高的问题，本文提出一种基于规则的区分性问题生成方法，无需标注数据即可借助问题生成系统澄清意图，并可扩展至多意图场景。
摘要：
任务导向对话系统通常采用意图检测系统，以便将用户查询映射到一组预定义意图。然而，自然语言中出现的用户查询很容易产生歧义，因此这种直接映射可能并不直接，会损害意图检测，并最终影响对话系统的整体性能。此外，获取特定领域的澄清问题成本很高。为了消除在两个意图之间存在歧义的查询，我们提出了一种生成区分性问题的新方法，该方法使用简单的基于规则的系统，能够利用任意问题生成系统，而不需要澄清问题的标注数据。我们的方法旨在区分两个意图，但可以很容易地扩展到针对多个意图的澄清。通过向用户寻求澄清来对用户意图进行分类，不仅有助于有效理解用户意图，而且降低了对话的机械感，使交互更加自然。
英文摘要：
Task oriented Dialogue Systems generally employ intent detection systems in order to map user queries to a set of pre-defined intents. However, user queries appearing in natural language can be easily ambiguous and hence such a direct mapping might not be straightforward harming intent detection and eventually the overall performance of a dialogue system. Moreover, acquiring domain-specific clarification questions is costly. In order to disambiguate queries which are ambiguous between two intents, we propose a novel method of generating discriminative questions using a simple rule based system which can take advantage of any question generation system without requiring annotated data of clarification questions. Our approach aims at discrimination between two intents but can be easily extended to clarification over multiple intents. Seeking clarification from the user to classify user intents not only helps understand the user intent effectively, but also reduces the roboticity of the conversation and makes the interaction considerably natural.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
