Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions
This work addresses the challenge of improving response quality in open-domain dialogues for users and developers, but it is incremental as it builds on existing research with new data and evaluation methods.
The paper tackled the problem of enabling open-domain dialogue systems to ask clarifying questions when user requests are ambiguous, by collecting and releasing a new dataset, benchmarking neural baselines, and proposing an evaluation pipeline for clarifying questions.
Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to provide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of 'asking clarifying questions in open-domain dialogues': (1) we collect and release a new dataset focused on open-domain single- and multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research.