Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues
This addresses information seeking in task-oriented dialogues for users and systems, but it is incremental as it builds on existing methods and datasets.
The paper tackles the problem of users' inability to describe complex information needs and ambiguous user profiles in task-oriented dialogue systems by proposing MAS2S, a Multi-Attention Seq2Seq Network that asks clarification questions, and it outperforms baselines on clarification question generation and answer prediction on a new dataset of about 100k dialogues.
Task-oriented dialogue systems aim at providing users with task-specific services. Users of such systems often do not know all the information about the task they are trying to accomplish, requiring them to seek information about the task. To provide accurate and personalized task-oriented information seeking results, task-oriented dialogue systems need to address two potential issues: 1) users' inability to describe their complex information needs in their requests; and 2) ambiguous/missing information the system has about the users. In this paper, we propose a new Multi-Attention Seq2Seq Network, named MAS2S, which can ask questions to clarify the user's information needs and the user's profile in task-oriented information seeking. We also extend an existing dataset for task-oriented information seeking, leading to the \ourdataset which contains about 100k task-oriented information seeking dialogues that are made publicly available\footnote{Dataset and code is available at \href{https://github.com/sweetalyssum/clarit}{https://github.com/sweetalyssum/clarit}.}. Experimental results on \ourdataset show that MAS2S outperforms baselines on both clarification question generation and answer prediction.