🤖 AI Summary
Deep learning models achieve state-of-the-art performance in NLP and information retrieval, yet their opacity severely hinders trustworthy deployment. This paper presents the first systematic, cross-model (word embeddings, RNNs/LSTMs, Transformers, BERT) and cross-task (text classification, question answering, document ranking) survey of interpretability methods in NLP/IR. We propose a structured taxonomy covering major paradigms—including feature attribution (e.g., LIME, SHAP), attention analysis, surrogate modeling, saliency mapping, and counterfactual explanation. Our framework constitutes the most comprehensive synthesis of textual interpretability techniques to date. We rigorously identify critical limitations—particularly the lack of standardized evaluation protocols and insufficient task-specific adaptation—and highlight key research gaps. The work establishes both theoretical foundations and practical guidelines for developing interpretable, reliable NLP systems.
📝 Abstract
Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. This article provides a broad overview of research on the explainability and interpretability of natural language processing and information retrieval methods. More specifically, we survey approaches that have been applied to explain word embeddings, sequence modeling, attention modules, transformers, BERT, and document ranking. The concluding section suggests some possible directions for future research on this topic.
