Noisy Perturbations for Estimating Query Difficulty in Dense Retrievers

Estimating query difficulty, also known as Query Performance Prediction (QPP), is concerned with assessing the retrieval quality of a ranking method for an input query. Most traditional unsupervised frequency-based models and many recent supervised neural methods have been designed specifically for predicting the performance of sparse retrievers such as BM25. In this paper we propose an unsupervised QPP method for dense neural retrievers which operates by redefining the well-known concept of query robustness i.e., a more robust query to perturbations is an easier query to handle. We propose to generate query perturbations for measuring query robustness by systematically injecting noise into the contextualized neural representation of each query. We then compare the retrieved list for the original query with that of the perturbed query as a way to measure query robustness. Our experiments on four different query sets including MS MARCO, TREC Deep Learning track 2019 and 2020 and TREC DL-Hard show consistently improved performance on linear and ranking correlation metrics over the state of the art.