A 'Pointwise-Query, Listwise-Document' based Query Performance Prediction Approach
Suchana Datta, Sean MacAvaney, Debasis Ganguly, Derek Greene · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022
The task of Query Performance Prediction (QPP) in Information Retrieval (IR) involves predicting the relative effectiveness of a search system for a given input query. Supervised approaches for QPP, such as NeuralQPP are often trained on pairs of queries to capture their relative retrieval performance. However, pointwise approaches, such as the recently proposed BERT-QPP, are generally preferable for efficiency reasons. In this paper, we propose a novel end-to-end neural cross-encoder-based approach that is trained pointwise on individual queries, but listwise over the top ranked documents (split into chunks). In contrast to prior work, the network is then trained to predict the number of relevant documents in each chunk for a given query. Our method is thus a split-n-merge technique that instead of predicting the likely number of relevant documents in the top-k, rather predicts the number of relevant documents for each fixed chunk size p(p