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.

翻译：基于深度学习和机器学习的模型在文本处理与信息检索领域已变得极为普及。然而，网络内部存在的非线性结构使得这些模型在很大程度上难以理解。大量研究致力于提升这些模型的透明度。本文对自然语言处理与信息检索方法的可解释性与可理解性研究进行了全面概述。具体而言，我们综述了应用于解释词嵌入、序列建模、注意力模块、Transformer、BERT以及文档排序的相关方法。结论部分针对该主题的未来研究方向提出了若干可能路径。