Cited By
View all- Rudra KFernando ZAnand A(2023)An in-depth analysis of passage-level label transfer for contextual document rankingInformation Retrieval10.1007/s10791-023-09430-526:1-2Online publication date: 8-Dec-2023
Data Augmentation for Sample Efficient and Robust Document Ranking
Authors: 
Abhijit Anand, Jurek Leonhardt, Jaspreet Singh, Koustav Rudra, 
Avishek AnandAuthors Info & Claims
Abhijit Anand, Jurek Leonhardt, Jaspreet Singh, Koustav Rudra, Avishek AnandAuthors Info & ClaimsArticle No.: 119, Pages 1 - 29
Abstract
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine-tuning. In this article, we propose data-augmentation methods for effective and robust ranking performance. One of the key benefits of using data augmentation is in achieving sample efficiency or learning effectively when we have only a small amount of training data. We propose supervised and unsupervised data augmentation schemes by creating training data using parts of the relevant documents in the query-document pairs. We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model. Our extensive experiments on subsets of the MS MARCO and TREC-DL test sets show that data augmentation, along with the ranking-adapted contrastive losses, results in performance improvements under most dataset sizes. Apart from sample efficiency, we conclusively show that data augmentation results in robust models when transferred to out-of-domain benchmarks. Our performance improvements in in-domain and more prominently in out-of-domain benchmarks show that augmentation regularizes the ranking model and improves its robustness and generalization capability.
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Publication History
Published: 29 April 2024
Online AM: 29 November 2023
Accepted: 20 November 2023
Revised: 15 August 2023
Received: 01 March 2023
Published in TOIS Volume 42, Issue 5
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View all- Rudra KFernando ZAnand A(2023)An in-depth analysis of passage-level label transfer for contextual document rankingInformation Retrieval10.1007/s10791-023-09430-526:1-2Online publication date: 8-Dec-2023
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