Abstract
Recent advances in deep learning have improved the performance of many Natural Language Processing (NLP) tasks such as translation, question-answering, and text classification. However, this improvement comes at the expense of model explainability. Black-box models make it difficult to understand the internals of a system and the process it takes to arrive at an output. Numerical (LIME, Shapley) and visualization (saliency heatmap) explainability techniques are helpful; however, they are insufficient because they require specialized knowledge. These factors led rationalization to emerge as a more accessible explainable technique in NLP. Rationalization justifies a model's output by providing a natural language explanation (rationale). Recent improvements in natural language generation have made rationalization an attractive technique because it is intuitive, human-comprehensible, and accessible to non-technical users. Since rationalization is a relatively new field, it is disorganized. As the first survey, rationalization literature in NLP from 2007-2022 is analyzed. This survey presents available methods, explainable evaluations, code, and datasets used across various NLP tasks that use rationalization. Further, a new subfield in Explainable AI (XAI), namely, Rational AI (RAI), is introduced to advance the current state of rationalization. A discussion on observed insights, challenges, and future directions is provided to point to promising research opportunities.
Abstract (translated)
最近的深度学习进展已经改善了许多自然语言处理(NLP)任务,例如翻译、问答和文本分类。然而,这种改进是以牺牲模型解释性为代价的。黑盒模型使得难以理解系统内部细节以及到达输出所需的过程。数值(LIME, Shapley)和可视化(saliency heatmap)解释性技术是有用的,但它们不足以满足需求,因为它们需要专业知识。这些因素导致解释性成为NLP中更容易解释的技术。通过提供自然语言解释,解释性解释了模型的输出(解释性)。最近的自然语言生成改进使得解释性成为吸引人的技术,因为它直观、人类可读,并适用于非技术人员。由于解释性是一个相对较新的领域,它缺乏组织。作为第一个调查,从2007年到2022年的解释性NLP文献被分析。该调查提供了可用的方法、可解释的评价、代码和数据集,用于使用解释性的各种NLP任务。此外,解释性AI(XAI)一个新的子领域,即解释性AI(RAI),被引入,以推进解释性当前状态。讨论观察到的见解、挑战和未来方向,以指出有前途的研究机会。
URL
https://arxiv.org/abs/2301.08912