We introduce the task of entity-centric query refinement. Given an input
query whose answer is a (potentially large) collection of entities, the task
output is a small set of query refinements meant to assist the user in
efficient domain exploration and entity discovery. We propose a method to
create a training dataset for this task. For a given input query, we use an
existing knowledge base taxonomy as a source of candidate query refinements,
and choose a final set of refinements from among these candidates using a
search procedure designed to partition the set of entities answering the input
query. We demonstrate that our approach identifies refinement sets which human
annotators judge to be interesting, comprehensive, and non-redundant. In
addition, we find that a text generation model trained on our newly-constructed
dataset is able to offer refinements for novel queries not covered by an
existing taxonomy. Our code and data are available at
this https URL.

该论文提出了实体为中心的查询细化任务，并提出了一种使用现有知识库分类法来产生查询细化数据集的方法，该方法可通过生成模型对新查询进行精细化，并证明了该方法的有效性。