Analyzing and Interpreting Convolutional Neural Networks in NLP
This work addresses the interpretability of CNNs in NLP, which is an incremental step toward improving model performance and explainability for researchers and practitioners.
The paper investigates whether convolutional neural networks (CNNs) model linguistic patterns like negation and clause compositionality in NLP tasks, applying visualization techniques to observe feature capture and error sources.
Convolutional neural networks have been successfully applied to various NLP tasks. However, it is not obvious whether they model different linguistic patterns such as negation, intensification, and clause compositionality to help the decision-making process. In this paper, we apply visualization techniques to observe how the model can capture different linguistic features and how these features can affect the performance of the model. Later on, we try to identify the model errors and their sources. We believe that interpreting CNNs is the first step to understand the underlying semantic features which can raise awareness to further improve the performance and explainability of CNN models.