Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions. Nevertheless, they are computationally expensive to create, and require a great deal of labeled data for specialized corpora. In this paper, we explore a low resource alternative which is a bag-of-embedding model for document retrieval and find that it is competitive with large transformer models fine tuned on information retrieval tasks. Our results show that a simple combination of TF-IDF, a traditional keyword matching method, with a shallow embedding model provides a low cost path to compete well with the performance of complex neural ranking models on 3 datasets. Furthermore, adding TF-IDF measures improves the performance of large-scale fine tuned models on these tasks.

在这篇论文中，我们探索了一种低资源替代方法，即基于词嵌入的文档检索模型，发现它在与信息检索任务上经过微调的大型Transformer模型相比具有竞争力。我们的结果表明，将TF-IDF（传统的关键词匹配方法）与浅层嵌入模型简单组合，提供了一种低成本的方法，使其在3个数据集上与复杂的神经排序模型的性能相媲美。此外，添加TF-IDF度量改进了这些任务上大规模微调模型的性能。

运用传统信息检索方法提升神经排序模型