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.

本文介绍了一种结构化语言模型，该模型将符号概率模型和神经网络相结合，以提高文本分类性能，并能够在自我监督的方式下学习预测组合树，仅需要原始文本和句级标签作为训练数据，从而具有一定的自我解释性。实验结果表明，本方法在下游任务中能够取得良好的预测准确性，同时预测的跨度标签与人类的推理有一定的一致性。