As the use of deep learning techniques has grown across various fields over the past decade, complaints about the opaqueness of the black-box models have increased, resulting in an increased focus on transparency in deep learning models. This work investigates various methods to improve the interpretability of deep neural networks for Natural Language Processing (NLP) tasks, including machine translation and sentiment analysis. We provide a comprehensive discussion on the definition of the term interpretability and its various aspects at the beginning of this work. The methods collected and summarised in this survey are only associated with local interpretation and are specifically divided into three categories: 1) interpreting the model's predictions through related input features; 2) interpreting through natural language explanation; 3) probing the hidden states of models and word representations.
From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?
School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）；Harvard Medical School（哈佛医学院）；Boston Children’s Hospital（波士顿儿童医院）；University of Southampton（南安普顿大学）；Athena Research Centre（雅典娜研究中心）
Explainable Natural Language Processing for Corporate Sustainability Analysis
College of Design and Engineering, National University of Singapore（新加坡国立大学设计与工程学院）；Asian Institute of Digital Finance, National University of Singapore（新加坡国立大学亚洲数字金融研究所）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科学技术研究局高性能计算研究所）；Sustainable and Green Finance Institute, National University of Singapore（新加坡国立大学可持续与绿色金融研究所）；NUS Business School, National University of Singapore（新加坡国立大学商学院）
KEDRec-LM: A Knowledge-distilled Explainable Drug Recommendation Large Language Model
Worcester Polytechnic Institute（伍斯特理工学院）；Yale University（耶鲁大学）；University of California, Davis（加州大学戴维斯分校）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
Explainable Natural Language Processing with Matrix Product States
Quantum Technology Foundation (Thailand)（泰国量子技术基金会）；Thailand Center of Excellence in Physics, Ministry of Higher Education, Science, Research and Innovation（高等教育、科学、研究和创新部泰国卓越物理中心）；Chulalongkorn University（朱拉隆功大学）；Mahidol University（玛希隆大学）；Blueqat Inc.（Blueqat公司）；Technical University of Crete（克里特理工大学）；National University of Singapore（新加坡国立大学）
ChartCheck: Explainable Fact-Checking over Real-World Chart Images
King’s College London（伦敦国王学院）；University of Utah（犹他大学）；University of Pennsylvania（宾夕法尼亚大学）；TIB – Leibniz Information Centre for Science and Technology（莱布尼茨科学与技术信息中心（TIB））
