  Product descriptions on e-commerce websites often suffer from missing
important aspects. Clarification question generation (CQGen) can be a promising
approach to help alleviate the problem. Unlike traditional QGen assuming the
existence of answers in the context and generating questions accordingly, CQGen
mimics user behaviors of asking for unstated information. The generated CQs can
serve as a sanity check or proofreading to help e-commerce merchant to identify
potential missing information before advertising their product, and improve
consumer experience consequently. Due to the variety of possible user
backgrounds and use cases, the information need can be quite diverse but also
specific to a detailed topic, while previous works assume generating one CQ per
context and the results tend to be generic. We thus propose the task of Diverse
CQGen and also tackle the challenge of specificity. We propose a new model
named KPCNet, which generates CQs with Keyword Prediction and Conditioning, to
deal with the tasks. Automatic and human evaluation on 2 datasets (Home &
Kitchen, Office) showed that KPCNet can generate more specific questions and
promote better group-level diversity than several competing baselines.
