论文标题
文本处理和检索方法的解释性：调查
Explainability of Text Processing and Retrieval Methods: A Survey
论文作者
论文摘要
深度学习和基于机器学习的模型在文本处理和信息检索中变得非常流行。但是，网络中存在的非线性结构使这些模型在很大程度上难以理解。大量研究的重点是提高这些模型的透明度。本文概述了关于自然语言处理和信息检索方法的解释性和解释性的研究。更具体地说，我们调查已应用于解释单词嵌入，序列建模，注意模块，变压器，BERT和文档排名的方法。总结部分提出了一些有关此主题的未来研究的可能方向。
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.
