ICTIR 2022 Talk: Sparse Pairwise Retrieval with Pretrained Transformers
Channel: webis
Date: 2022-07-11

Title:Sparse Pairwise Retrieval with Pretrained Transformers
Authors: Lukas Gienapp, Maik Fröbe, Matthias Hagen, and Martin Potthast
Conference: ICTIR 2022
Paper: https://webis.de/publications.html?q=ICTIR%2B22#gienapp_2022a
GitHub – https://github.com/webis-de/ICTIR-22/

Abstract:
Pairwise re-ranking models predict which of two documents is more relevant to a query and then aggregate a final ranking from such preferences. This is often more effective than pointwise reranking models that directly predict a relevance value for each document. However, the high inference overhead of pairwise models limits their practical application: usually, for a set of k documents to be re-ranked, preferences for all k^2 −k comparison pairs excluding self- comparisons are aggregated. We investigate whether the efficiency of pairwise re-ranking can be improved by sampling from all pairs. In an exploratory study, we evaluate three sampling methods and five preference aggregation methods. The best combination allows for an order of magnitude fewer comparisons at an acceptable loss of retrieval effectiveness, while competitive effectiveness is already achieved with about one third of the comparisons.