  Clarification is increasingly becoming a vital factor in various topics of
information retrieval, such as conversational search and modern Web search
engines. Prompting the user for clarification in a search session can be very
beneficial to the system as the user's explicit feedback helps the system
improve retrieval massively. However, it comes with a very high risk of
frustrating the user in case the system fails in asking decent clarifying
questions. Therefore, it is of great importance to determine when and how to
ask for clarification. To this aim, in this work, we model search clarification
prediction as user engagement problem. We assume that the better a
clarification is, the higher user engagement with it would be. We propose a
Transformer-based model to tackle the task. The comparison with competitive
baselines on large-scale real-life clarification engagement data proves the
effectiveness of our model. Also, we analyse the effect of all result page
elements on the performance and find that, among others, the ranked list of the
search engine leads to considerable improvements. Our extensive analysis of
task-specific features guides future research.
