  Users often need to look through multiple search result pages or reformulate
queries when they have complex information-seeking needs. Conversational search
systems make it possible to improve user satisfaction by asking questions to
clarify users' search intents. This, however, can take significant effort to
answer a series of questions starting with "what/why/how". To quickly identify
user intent and reduce effort during interactions, we propose an intent
clarification task based on yes/no questions where the system needs to ask the
correct question about intents within the fewest conversation turns. In this
task, it is essential to use negative feedback about the previous questions in
the conversation history. To this end, we propose a Maximum-Marginal-Relevance
(MMR) based BERT model (MMR-BERT) to leverage negative feedback based on the
MMR principle for the next clarifying question selection. Experiments on the
Qulac dataset show that MMR-BERT outperforms state-of-the-art baselines
significantly on the intent identification task and the selected questions also
achieve significantly better performance in the associated document retrieval
tasks.
