Many open-domain questions are under-specified and thus have multiple
possible answers, each of which is correct under a different interpretation of
the question. Answering such ambiguous questions is challenging, as it requires
retrieving and then reasoning about diverse information from multiple passages.
We present a new state-of-the-art for answering ambiguous questions that
exploits a database of unambiguous questions generated from Wikipedia. On the
challenging ASQA benchmark, which requires generating long-form answers that
summarize the multiple answers to an ambiguous question, our method improves
performance by 15% (relative improvement) on recall measures and 10% on
measures which evaluate disambiguating questions from predicted outputs.
Retrieving from the database of generated questions also gives large
improvements in diverse passage retrieval (by matching user questions q to
passages p indirectly, via questions q' generated from p).

通过利用来自维基百科的明确问题数据库，我们提出了一个新的用于回答含糊问题的最新方法，在含糊问题的多个答案的总结中，我们的方法在回收率指标上提高了 15%（相对改进），在评估从预测输出中消除模糊问题的指标上提高了 10%。从生成的问题数据库检索还可以在多样的篇章检索中取得巨大的改进。

利用包含问题、答案和修订的数据库回答模棱两可的问题

Answering Ambiguous Questions with a Database of Questions, Answers, and  Revisions

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.

本文提出了通过增强学习模型来澄清模糊问题的方法，包括分割问题、选择标签、确认意图和构建适当的响应。模型基于深度政策网络的强化学习模型，通过真实用户点击数据评估并展示了显著的性能提升。