论文标题
通过加强学习在对话中的互动问题澄清
Interactive Question Clarification in Dialogue via Reinforcement Learning
论文作者
论文摘要
在现实世界对话系统中，应对歧义问题一直是一个多年生问题。尽管提出问题的澄清是人类互动的一种常见形式，但很难定义适当的问题来引起用户更具体的意图。在这项工作中，我们提出了一个加强模型，以通过建议对原始查询的改进来澄清歧义问题。我们首先制定一个集合分区问题，以选择一组标签，使我们能够区分潜在的明确意图。我们将所选标签列为用户的意图短语，以进一步确认。然后，选定的标签与原始用户查询一起用作精制查询，可以更轻松地确定合适的响应。该模型是使用深厚政策网络的加固学习训练的。我们根据现实世界用户点击评估模型，并在几个不同的实验中展示了重大改进。
Coping with ambiguous questions has been a perennial problem in real-world dialogue systems. Although clarification by asking questions is a common form of human interaction, it is hard to define appropriate questions to elicit more specific intents from a user. In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query. We first formulate a collection partitioning problem to select a set of labels enabling us to distinguish potential unambiguous intents. We list the chosen labels as intent phrases to the user for further confirmation. The selected label along with the original user query then serves as a refined query, for which a suitable response can more easily be identified. The model is trained using reinforcement learning with a deep policy network. We evaluate our model based on real-world user clicks and demonstrate significant improvements across several different experiments.
