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.

过去十年，随着深度学习技术在各个领域的使用不断增长，对黑盒模型不透明性的抱怨也随之增加，导致人们越来越关注深度学习模型的透明度。这项工作研究了提高深度神经网络对自然语言处理（NLP）任务的可解释性的各种方法，包括机器翻译和情感分析。我们在本文的开头对术语可解释性的定义及其各个方面进行了全面的讨论。本次调查收集和总结的方法仅与局部解释相关，具体分为三类：（1）通过相关输入特征解释模型的预测； （二）通过自然语言解释进行口译； (3) 探索模型和词表示的隐藏状态。