将密集的检索解释为主题的混合物

密集检索(DR)在第一阶段的检索中达到了最先进的结果，但我们对促成其成功的机制知之甚少。因此，在这项工作中，我们对最近提出的DR模型进行了解释研究。具体来说，我们首先将文档和查询编码器的嵌入输出离散化。基于离散表示，我们分析了输入标记的归属。对公共试验收集品进行了定性和定量实验。结果表明，DR模型注意输入的不同方面，并提取各种高级主题表示。因此，我们可以将DR模型学习到的表示看作是高级主题的混合物。

原文题目：Interpreting Dense Retrieval as Mixture of Topics

原文：Dense Retrieval (DR) reaches state-of-the-art results in first-stage retrieval, but little is known about the mechanisms that contribute to its success. Therefore, in this work, we conduct an interpretation study of recently proposed DR models. Specifically, we first discretize the embeddings output by the document and query encoders. Based on the discrete representations, we analyze the attribution of input tokens. Both qualitative and quantitative experiments are carried out on public test collections. Results suggest that DR models pay attention to different aspects of input and extract various high-level topic representations. Therefore, we can regard the representations learned by DR models as a mixture of high-level topics.