利用自适应反射增强预训练深度学习模型

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xinzhi Wang, Mengyue Li, Hang Yu, Chenyang Wang, Vijayan Sugumaran, Hui Zhang
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引用次数: 0

摘要

在文本挖掘领域,流行的深度学习模型主要侧重于将输入特征映射到预测输出结果,在自我辩证思维过程中表现出不足。受人类认知中自我反思机制的启发,我们提出了一种假设,即现有模型可模仿决策过程并自动纠正错误预测。我们引入了自适应反思增强型预训练深度学习模型(S-REM)来验证我们的假设,并确定值得复制的知识类型。在预训练模型的基础上,S-REM 引入了伪标签的局部解释和所有标签的全局解释作为解释知识。TF-IDF 模型中的关键词知识也被整合进来,形成反映知识。基于关键解释特征,预训练模型通过两种反思方法对初始决策进行反思,并优化深度学习模型的预测。在四个数据集(包括三个公共数据集和一个私有数据集)的两个文本挖掘任务中,对 S-REM 的局部和全局反射变体进行了实验。实验结果表明,我们的方法能有效提高最先进的深度学习模型的准确性。此外,该方法还可以通过与各种深度学习模型的整合,为开发可解释性奠定基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhancing Pre-trained Deep Learning Model with Self-Adaptive Reflection

Enhancing Pre-trained Deep Learning Model with Self-Adaptive Reflection

In the text mining area, prevalent deep learning models primarily focus on mapping input features to result of predicted outputs, which exhibit a deficiency in self-dialectical thinking process. Inspired by self-reflective mechanisms in human cognition, we propose a hypothesis that existing models emulate decision-making processes and automatically rectify erroneous predictions. The Self-adaptive Reflection Enhanced pre-trained deep learning Model (S-REM) is introduced to validate our hypotheses and to determine the types of knowledge that warrant reproduction. Based on the pretrained-model, S-REM introduces the local explanation for pseudo-label and the global explanation for all labels as the explanation knowledge. The keyword knowledge from TF-IDF model is also integrated to form a reflection knowledge. Based on the key explanation features, the pretrained-model reflects on the initial decision by two reflection methods and optimizes the prediction of deep learning models. Experiments with local and global reflection variants of S-REM on two text mining tasks across four datasets, encompassing three public and one private dataset were conducted. The outcomes demonstrate the efficacy of our method in improving the accuracy of state-of-the-art deep learning models. Furthermore, the method can serve as a foundational step towards developing explainable through integration with various deep learning models.

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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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