论文标题
通过检索歧视性澄清问题来解决意图歧义
Resolving Intent Ambiguities by Retrieving Discriminative Clarifying Questions
论文作者
论文摘要
面向任务的对话系统通常采用意图检测系统，以将用户查询映射到一组预定义的意图。但是，以自然语言出现的用户查询很容易模棱两可，因此，这种直接映射可能不会直接损害意图检测，最终不是对话系统的整体性能。此外，获取特定领域的澄清问题是昂贵的。为了消除两个意图之间模棱两可的查询，我们提出了一种新颖的方法，即使用基于简单的规则的系统来生成歧视性问题，该系统可以利用任何问题的生成系统而无需澄清问题的注释数据。我们的方法旨在歧视两种意图，但很容易扩展到多种意图上的澄清。寻求用户澄清以对用户的意图进行分类，这不仅有助于有效地了解用户意图，还可以降低对话的机器人性，并使交互变得非常自然。
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.
