Cost-effective construction of Information Retrieval test collections

Abstract

In this paper we describe our recent research on effective construction of Information Retrieval collections. Relevance assessments are a core component of test collections, but they are expensive to produce. For each test query, only a sample of documents in the corpus can be assessed for relevance. We discuss here a class of document adjudication methods that iteratively choose documents based on reinforcement learning. Given a pool of candidate documents supplied by multiple retrieval systems, the production of relevance assessments is modeled as a multi-armed bandit problem. These bandit-based algorithms identify relevant documents with minimal effort. One instance of these models has been adopted by NIST to build the test collection of the TREC 2017 common core track.