  Conversational information seeking (CIS) is playing an increasingly important
role in connecting people to information. Due to the lack of suitable resource,
previous studies on CIS are limited to the study of theoretical/conceptual
frameworks, laboratory-based user studies, or a particular aspect of CIS (e.g.,
asking clarifying questions). In this work, we make efforts to facilitate
research on CIS from three aspects. (1) We formulate a pipeline for CIS with
six sub-tasks: intent detection (ID), keyphrase extraction (KE), action
prediction (AP), query selection (QS), passage selection (PS), and response
generation (RG). (2) We release a benchmark dataset, called wizard of search
engine (WISE), which allows for comprehensive and in-depth research on all
aspects of CIS. (3) We design a neural architecture capable of training and
evaluating both jointly and separately on the six sub-tasks, and devise a
pre-train/fine-tune learning scheme, that can reduce the requirements of WISE
in scale by making full use of available data. We report some useful
characteristics of CIS based on statistics of WISE. We also show that our best
performing model variant isable to achieve effective CIS as indicated by
several metrics. We release the dataset, the code, as well as the evaluation
scripts to facilitate future research by measuring further improvements in this
important research direction.
