  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.
