Understanding Convolutional Neural Networks for Text Classification (1809.08037v3)

Abstract: We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vision can be interpreted by projecting filters into image space, but for discrete sequence inputs CNNs remain a mystery. We aim to understand the method by which the networks process and classify text. We examine common hypotheses to this problem: that filters, accompanied by global max-pooling, serve as ngram detectors. We show that filters may capture several different semantic classes of ngrams by using different activation patterns, and that global max-pooling induces behavior which separates important ngrams from the rest. Finally, we show practical use cases derived from our findings in the form of model interpretability (explaining a trained model by deriving a concrete identity for each filter, bridging the gap between visualization tools in vision tasks and NLP) and prediction interpretability (explaining predictions). Code implementation is available online at github.com/sayaendo/interpreting-cnn-for-text.

• The paper shows that max-pooling in CNNs functions as a threshold filter, allowing nearly 40% of pooled ngrams to be disregarded without performance loss.
• The paper uncovers that individual CNN filters often capture multiple distinct semantic classes, challenging the assumption of filter homogeneity.
• The paper advances interpretability by linking model-level and prediction-level insights to clearer explanations of CNN decision processes.

Understanding Convolutional Neural Networks for Text Classification

The paper "Understanding Convolutional Neural Networks for Text Classification" presents a comprehensive analysis of the mechanisms underlying Convolutional Neural Networks (CNNs) when applied to NLP, specifically focusing on text classification. CNNs, originally developed for image processing, have demonstrated their utility in text-related tasks; however, the interpretation of CNNs in NLP, due to the discrete nature of text data, remains complex. This paper provides a thorough examination of how CNNs process and classify text, challenging existing assumptions and adding depth to the current understanding of model interpretability.

Key Contributions

1. Ngram Detection and Max-Pooling: The paper investigates the hypothesis that CNN filters act as ngram detectors, with max-pooling employed to highlight the most relevant ngrams for classification. The findings clarify that max-pooling introduces a thresholding behavior, effectively discriminating between significant and insignificant ngrams. It experimentally shows that roughly 40% of pooled ngrams on average can be disregarded without degrading model performance, indicating many ngrams do not meaningfully contribute to the classification outcome.
2. Filter Characteristics: Contrary to common assumptions that filters are homogeneous, specializing in closely related ngrams, the paper reveals that filters often capture several distinct semantic classes. This is achieved through different activation patterns across slots in a filter. The presence of such patterns showcases the capacity of single filters to detect multiple ngram families and suppress negated semantic classes.
3. Interpretability Improvements: Utilizing these insights, the paper proposes advancements in both model-level and prediction-level interpretability. Model-level interpretability benefits from the derivation of concrete identities for each filter, offering insights akin to visualization techniques in vision networks. For prediction-level interpretability, focusing on significant ngrams and accounting for negative cues provide a clearer explanation of model decisions.

Methodological Approach

The authors employ a meticulous analysis utilizing pre-trained GloVe embeddings, applying different convolutional window sizes and multiple filters to discern activation behaviors. By examining word-level slot activations instead of aggregate ngram scores, the research distinguishes between naturally occurring ngrams and potentially misleading, high-scoring constructed ngrams.

Implications and Future Directions

The insights provided by this research have practical and theoretical implications, particularly in improving the transparency and trustworthiness of neural models in NLP. By demystifying the inner workings of CNNs in discrete sequence spaces, this paper lays the groundwork for more refined methods of model interpretability. Additionally, identifying the adversarial potential in over-maximized slot activations opens pathways for future research in model robustness and adversarial defenses in NLP tasks.

Overall, the paper significantly contributes to the interpretability of CNNs in NLP, challenging prevailing assumptions and suggesting empirical methods for more in-depth understanding and transparency in neural model predictions. Future research can expand on these findings to explore the robustness of CNNs against adversarial examples and apply these interpretability methods to other sequence modeling tasks beyond text classification.