A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classification

Xiang Hu, XinYu KONG, Kewei Tu

Keywords: Interpretability, natural language processing, text classification, unsupervised learning, structured language model, multiple instance learning, recursive neural network

TL;DR: An inherently interpretable model architecture with explicit unsupervised label to constituent alignments.

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.

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Please Choose The Closest Area That Your Submission Falls Into: Unsupervised and Self-supervised learning