Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable. This survey presents an overview of the current state of Explainable AI (XAI), considered within the domain of Natural Language Processing (NLP). We discuss the main categorization of explanations, as well as the various ways explanations can be arrived at and visualized. We detail the operations and explainability techniques currently available for generating explanations for NLP model predictions, to serve as a resource for model developers in the community. Finally, we point out the current gaps and encourage directions for future work in this important research area.

翻译：近些年来,最新模型的质量有了重大进步,但这是以降低模型解释能力为代价的,这次调查概述了在自然语言处理领域考虑的可解释的AI(XAI)的现状,我们讨论了解释的主要分类,以及可以得出和可视化的各种解释方式,我们详细介绍了目前可用于解释NLP模型预测的操作和可解释技术,作为社区模型开发者的资源。最后,我们指出了目前的差距,并鼓励为这一重要研究领域今后的工作指明方向。