论文标题
使用Grad-CAM解释神经排名模型
Interpreting Neural Ranking Models using Grad-CAM
论文作者
论文摘要
最近，在IR中应用深度神经网络已成为一个重要且及时的主题。例如，与传统排名模型相比，神经排名模型（NRMS）表现出了有希望的性能。但是，由于神经网络的复杂结构，NRM解释排名结果变得更加困难。另一方面，包括Grad-CAM在内的可解释机器学习（IML）正在进行大量研究。 Grad-CAM是一种归因方法，它可以可视化有助于网络输出的输入区域。在本文中，我们采用Grad-CAM来解释NRM的排名结果。通过采用Grad-CAM，我们分析了每个查询文档对如何为给定的查询和文档的匹配分数贡献。可视化结果提供了有关为什么某个文档与给定查询相关的见解。此外，结果表明，神经排名模型捕获了相关性的微妙概念。我们的解释方法和可视化结果可用于片段生成和用户疑问意图分析。
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.
