Towards Asking Clarification Questions for Information Seeking on
  Task-Oriented Dialogues
Yue Feng Hossein A. Rahmani Aldo Lipani Emine Yilmaz
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.