Quam

Abstract

A central task in information retrieval and the NLP communities is relevance modeling, which aims to rank documents based on their expressed information needs Many knowledge-intensive retrieval tasks are powered by a first-stage retrieval stage for context selection, followed by a more involved task-specific model. However, using this filtering (cascading) approach inherently limits the recall of subsequent stages. Recently, adaptive re-ranking techniques have been proposed to overcome this issue by continually selecting documents from the whole corpus, rather than only considering an initial pool of documents. However, so far these approaches have been limited to heuristic design choices, particularly in terms of the criteria for document selection. In this work, we propose a unifying view of the nascent area of adaptive retrieval by proposing Quam, a query-affinity model of adaptive re-ranking that includes two complementary components: (1) a more principled algorithm for document selection, and (2) a data-driven approach to model document co-relevance during indexing. Our extensive experimental evidence shows that our proposed approach improves the recall performance by up to 26% over the standard re-ranking baselines. Further, the query affinity modelling and relevance-aware document graph components can be injected into any adaptive retrieval approach. The experimental results show the existing adaptive retrieval approach improves recall by up to 12%.