Conceitos Básicos
The proposed counterfactual framework can identify the terms that need to be added to a document to improve its ranking with respect to a specific retrieval model and query.
Resumo
The paper introduces a counterfactual explanation framework for information retrieval (IR) models, which aims to identify the terms that need to be added to a document to improve its ranking for a given query and retrieval model.
The key highlights are:
The authors propose a model-agnostic counterfactual framework to explain the non-relevance of a document for a given query and retrieval model. This is in contrast to existing explainable IR (ExIR) approaches that focus on explaining the relevance of documents.
The framework uses a constrained optimization setup to generate counterfactual examples, which are then used to train a classifier that can predict whether a document will be ranked within the top-K results for a given query and retrieval model.
Experiments are conducted on the MS MARCO passage and document ranking datasets using four different retrieval models (BM25, DRMM, DSSM, and ColBERT). The results show that the proposed counterfactual framework outperforms intuitive baselines in terms of the fidelity of the explanations, the diversity of the suggested terms, and the average rank shift of the documents.
The authors also provide a sensitivity analysis of the key parameters in the counterfactual framework, such as the number of documents used to train the classifier and the number of counterfactuals generated.
Overall, the proposed counterfactual explanation framework provides a novel approach to understanding the non-relevance of documents in IR models, which can help IR practitioners improve the performance of their retrieval systems.
Estatísticas
The average length of queries in the MS MARCO passage dataset is 5.9 words, and the average length of documents is 64.9 words.
The average length of queries in the MS MARCO document dataset is 6.9 words, and the average length of documents is 1134.2 words.
Citações
"The fundamental research question which we address in this research work is described as follows. RQ1: What are the terms that should be added to a document which can push the document to a higher rank with respect to a particular retrieval model?"
"To the best of our knowledge, we mark the first attempt to tackle this specific counterfactual problem."