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. This paper proposes a simple yet effective method to improve ranking performance on smaller datasets using supervised contrastive learning for the document ranking problem. We perform data augmentation by creating training data using parts of the relevant documents in the query-document pairs. We then use a supervised contrastive learning objective to learn an effective ranking model from the augmented dataset. Our experiments on subsets of the TREC-DL dataset show that, although data augmentation leads to an increasing the training data sizes, it does not necessarily improve the performance using existing pointwise or pairwise training objectives. However, our proposed supervised contrastive loss objective leads to performance improvements over the standard non-augmented setting showcasing the utility of data augmentation using contrastive losses. Finally, we show the real benefit of using supervised contrastive learning objectives by showing marked improvements in smaller ranking datasets relating to news (Robust04), finance (FiQA), and scientific fact checking (SciFact).
|Title of host publication||ICTIR 2022 - Proceedings of the 2022 ACM SIGIR International Conference on the Theory of Information Retrieval|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||11|
|Publication status||Published - 2022|
|Event||8th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2022 - Virtual, Online, Spain|
Duration: 11 Jul 2022 → 12 Jul 2022
|Name||ICTIR 2022 - Proceedings of the 2022 ACM SIGIR International Conference on the Theory of Information Retrieval|
|Conference||8th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2022|
|Period||11/07/22 → 12/07/22|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- data augmentation
- document ranking
- ranking performance
- supervised contrastive loss