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Rationalization for Explainable NLP: A Survey

2023-01-21 07:58:03
Sai Gurrapu, Ajay Kulkarni, Lifu Huang, Ismini Lourentzou, Laura Freeman, Feras A. Batarseh

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

PDF

https://arxiv.org/pdf/2301.08912.pdf


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