Towards Facet-Driven Generation of Clarifying Questions for Conversational Search

Abstract

Clarifying an underlying user information need is an important aspect of a modern-day IR system. The importance of clarification is even higher in limited-bandwidth scenarios, such as conversational or mobile search, where a user is unable to easily browse through a long list of retrieved results. Thus, asking clarifying questions about user's potentially ambiguous queries arises as one of the main tasks of conversational search. Recent approaches have, while making significant progress in the field, remained limited to selecting a clarifying question from a predefined set or prompting the user with vague or template-based questions. However, with the recent advances in text generation through large-scale language models, an ideal system should generate the next clarifying question. The challenge of generating an appropriate clarifying question is twofold: 1) to produce the question in coherent natural language; 2) to ask a question that is relevant to the user query. In this paper, we propose a model that generates clarifying questions with respect to the user query and query facets. We fine-tune the GPT-2 language model to generate questions related to the query and one of the extracted query facets. Compared to competitive baselines, results show that our proposed method is both natural and useful, as judged by human annotators. Moreover, we discuss the potential theoretical framework this approach would fit in. We release the code for future work and reproducibility purposes.