从输出到洞察力:对可解释文本分类的合理化方法的调查
Erick Mendez Guzman1, Viktor Schlegel1,2, Riza Batista-Navarro1
1Department of Computer Science, The University of Manchester, Manchester, United Kingdom.
Frontiers in artificial intelligence
|August 7, 2024
概括
本调查探讨了文本分类的合理化方法,重点是人类可以理解的语言解释 (rationales). 它审查了技术,数据集和评估指标,以提高模型的可解释性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习可解释性 机器学习可解释性
背景情况:
- 深度学习模型在文本分类方面表现出色,但缺乏透明度,阻碍了在关键应用中的使用.
- 可解释的人工智能 (XAI) 是至关重要的,基于语言的推理对于用户来说非常直观.
研究的目的:
- 调查文本分类的合理化技术的最新情况.
- 为推理生成提供方法,数据集和评估指标的全面概述.
主要方法:
- 提取和抽象的逻辑生成方法的分类和比较.
- 审查现有的理由注释数据集用于培训和评估.
- 基于代理和基于人类的指标的描述,用于评估逻辑质量.
主要成果:
- 该调查综合了创建文本分类模型可理解解释的进展.
- 它强调了专门数据集和强大的评估指标对合理化的重要性.
结论:
- 合理化为实现更易于解释的文本分类系统提供了一个有希望的途径.
- 未来的工作应该解决当前的挑战,以进一步推进可解释的人工智能领域.
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