QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations
This addresses a specific challenge in information retrieval for users needing precise entity searches, but it is incremental as it focuses on dataset creation and benchmarking.
The paper tackles the problem of retrieval systems handling queries with implicit set operations like intersection and negation, by constructing QUEST, a dataset of 3357 natural language queries mapped to Wikipedia entities, and finds that modern systems often struggle, especially with negation and conjunction.
Formulating selective information needs results in queries that implicitly specify set operations, such as intersection, union, and difference. For instance, one might search for "shorebirds that are not sandpipers" or "science-fiction films shot in England". To study the ability of retrieval systems to meet such information needs, we construct QUEST, a dataset of 3357 natural language queries with implicit set operations, that map to a set of entities corresponding to Wikipedia documents. The dataset challenges models to match multiple constraints mentioned in queries with corresponding evidence in documents and correctly perform various set operations. The dataset is constructed semi-automatically using Wikipedia category names. Queries are automatically composed from individual categories, then paraphrased and further validated for naturalness and fluency by crowdworkers. Crowdworkers also assess the relevance of entities based on their documents and highlight attribution of query constraints to spans of document text. We analyze several modern retrieval systems, finding that they often struggle on such queries. Queries involving negation and conjunction are particularly challenging and systems are further challenged with combinations of these operations.