Interpretability of Retrieval Models
The objective of the proposal is to understand and address key challenges in interpreting retrieval or ranking models that are considered to be central in IR.
One of the main challenges in IR is in understanding the true information need or intent of the user that is typically expressed in the form of an under-specified query for the task of document ranking. To deal with this, ranking models employ extensive query modelling, exploit contextual information and learn from explicit user feedback to improve their understanding of the underlying user intent. As a consequence, modern ranking approaches like learning to rank and neural models have become effective but increasingly more opaque.
In this proposal, we intend to interpret latent query intents, context, and feature attributions of already trained models in a post-hoc manner.