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。