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 paper, 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.

翻译：上下文排序模型在文档排序任务中相比传统模型取得了显著的性能提升。然而，这些高度过参数化的模型往往对数据需求量大，即使在进行微调时也需要大量数据。本文针对有效且鲁棒的排序性能，提出了数据增强方法。数据增强的一个关键优势在于实现样本高效性，即仅在少量训练数据的情况下也能有效学习。我们通过利用查询-文档对中相关文档的部分内容来创建训练数据，提出了有监督和无监督的数据增强方案。随后，我们针对文档排序任务适配了一系列对比损失函数，这些损失函数能够利用增强数据来学习有效的排序模型。我们在MS MARCO和TREC-DL测试集的子集上进行的广泛实验表明，数据增强与经排序适配的对比损失相结合，在大多数数据集规模下均能带来性能提升。除了样本高效性之外，我们还最终证明，当迁移至领域外基准测试时，数据增强能够产生鲁棒的模型。我们在领域内以及更显著地在领域外基准测试上的性能提升表明，增强技术对排序模型起到了正则化作用，提升了其鲁棒性和泛化能力。