Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions
arXiv:2109.05794
Abstract
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.
Accepted in EMNLP 2021
References in corpus (6)
- Towards a Human-like Open-Domain Chatbot
- Dialogue Learning With Human-In-The-Loop
- Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search
- ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)
- A Clarifying Question Selection System from NTES_ALONG in Convai3 Challenge
- Ranking Clarifying Questions Based on Predicted User Engagement