使用Grad-CAM解释神经排序模型
Interpreting Neural Ranking Models using Grad-CAM
摘要：
近年来，将深度神经网络应用于信息检索已成为一个重要而及时的课题。例如，与传统的排名模型相比，神经排名模型（NRM）已经显示出有前途的性能。然而，由于神经网络的复杂结构，解释NRM的排名结果变得更加困难。另一方面，大量关于可解释机器学习（IML）的研究正在进行中，包括Grad-CAM。Grad-CAM是一种属性方法，它可以可视化对网络输出有贡献的输入区域。在本文中，我们采用Grad-CAM解释NRM的排名结果。通过采用Grad-CAM，我们分析了每个查询文档术语对如何对给定的查询和文档对的匹配得分做出贡献。可视化结果提供了关于为什么某个文档与给定查询相关的见解。此外，结果表明，神经排名模型捕捉到了相关性的微妙概念。我们的解释方法和可视化结果可以用于片段生成和用户查询意图分析。
英文摘要：
Recently, applying deep neural networks in IR has become an important and timely topic. For instance, Neural Ranking Models(NRMs) have shown promising performance compared to the traditional ranking models. However, explaining the ranking results has become even more difficult with NRM due to the complex structure of neural networks. On the other hand, a great deal of research is under progress on Interpretable Machine Learning(IML), including Grad-CAM. Grad-CAM is an attribution method and it can visualize the input regions that contribute to the network's output. In this paper, we adopt Grad-CAM for interpreting the ranking results of NRM. By adopting Grad-CAM, we analyze how each query-document term pair contributes to the matching score for a given pair of query and document. The visualization results provide insights on why a certain document is relevant to the given query. Also, the results show that neural ranking model captures the subtle notion of relevance. Our interpretation method and visualization results can be used for snippet generation and user-query intent analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
