Abstract
Deep neural networks based on layer-stacking architectures have historically suffered from poor inherent interpretability. Meanwhile, symbolic probabilistic models function with clear interpretability, but how to combine them with neural networks to enhance their performance remains to be explored. In this paper, we try to marry these two systems for text classification via a structured language model. We propose a Symbolic-Neural model that can learn to explicitly predict class labels of text spans from a constituency tree without requiring any access to span-level gold labels. As the structured language model learns to predict constituency trees in a self-supervised manner, only raw texts and sentence-level labels are required as training data, which makes it essentially a general constituent-level self-interpretable classification model. Our experiments demonstrate that our approach could achieve good prediction accuracy in downstream tasks. Meanwhile, the predicted span labels are consistent with human rationales to a certain degree.
Abstract (translated)
基于层堆架构的深度学习网络历史上一直存在缺乏内在解释性的问题。与此同时,符号概率模型具有明确的解释性,但如何将它们与神经网络结合以提高其性能仍然是待探索的。在本文中,我们尝试通过结构语言模型将这两个系统结合起来进行文本分类。我们提出了一种符号神经网络模型,可以 explicitly predict class labels of text spans from a constituency tree,而不需要访问span级别的黄金标签。由于结构语言模型在自我监督的情况下学习预测 constituency 树,只需要原始文本和句子级别的标签作为训练数据,因此它本质上是一个通用组成部分级别的自解释性分类模型。我们的实验结果表明,我们的方法可以在下游任务中获得良好的预测精度。与此同时,预测的跨度标签在一定程度上与人类理由一致。
URL
https://arxiv.org/abs/2303.02860
