Interpretability in Neural Information Retrieval

Neural information retrieval (IR) has transitioned from using classical human-defined relevance rules to leveraging complex neural models for retrieval tasks. While benefiting from advances in machine learning (ML), neural IR also inherits several drawbacks, including the opacity of the model’s decision-making process. This thesis aims to tackle this issue and enhance the transparency of neural IR models. Particularly, our work focuses on understanding which input features neural ranking models rely on to generate a specific ranking list. Our work draws inspiration from interpretable ML. However, we also recognize the unique aspects of IR tasks, which guide our development of methods specifically designed to interpret IR models....