The Simplest Thing That Can Possibly Work: (Pseudo-)Relevance Feedback via Text Classification
Xiao Han, Yuqi Liu, Jimmy Lin · 2021
Motivated by recent commentary that has questioned today's pursuit of ever-more complex models and mathematical formalisms in applied machine learning and whether meaningful empirical progress is actually being made, this paper tackles the decades-old problem of pseudo-relevance feedback with "the simplest thing that can possibly work". We present a technique based on training a document relevance classifier for each information need using pseudo-labels from an initial ranked list and then applying the classifier to rerank the retrieved documents. Experiments demonstrate significant improvements across a number of standard newswire collections, with initial rankings supplied by bag-of-words BM25 as well as from query expansion. Further evaluations in the TREC-COVID challenge using human relevance judgments verify the effectiveness and robustness of our proposed technique. While this simple idea draws elements from several well-known threads in the literature, to our knowledge this exact combination has not previously been proposed and rigorously evaluated.