  Conversational Information Seeking (CIS) is a relatively new research area
within conversational AI that attempts to seek information from end-users in
order to understand and satisfy users' needs. If realized, such a system has
far-reaching benefits in the real world; for example, a CIS system can assist
clinicians in pre-screening or triaging patients in healthcare. A key open
sub-problem in CIS that remains unaddressed in the literature is generating
Information Seeking Questions (ISQs) based on a short initial query from the
end-user. To address this open problem, we propose Information SEEking Question
generator (ISEEQ), a novel approach for generating ISQs from just a short user
query, given a large text corpus relevant to the user query. Firstly, ISEEQ
uses a knowledge graph to enrich the user query. Secondly, ISEEQ uses the
knowledge-enriched query to retrieve relevant context passages to ask coherent
ISQs adhering to a conceptual flow. Thirdly, ISEEQ introduces a new deep
generative-adversarial reinforcement learning-based approach for generating
ISQs. We show that ISEEQ can generate high-quality ISQs to promote the
development of CIS agents. ISEEQ significantly outperforms comparable baselines
on five ISQ evaluation metrics across four datasets having user queries from
diverse domains. Further, we argue that ISEEQ is transferable across domains
for generating ISQs, as it shows the acceptable performance when trained and
tested on different pairs of domains. The qualitative human evaluation confirms
ISEEQ-generated ISQs are comparable in quality to human-generated questions and
outperform the best comparable baseline.

Information Retrieval;Artificial Intelligence;Computation and Language