A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training

TL;DR

This paper introduces a visual interpretation-based self-improving classification system combining BERT and virtual adversarial training to enhance interpretability and robustness in text classification tasks.

Contribution

It proposes a novel semi-supervised framework that integrates visualization techniques with VAT and BERT for improved classification and interpretability.

Findings

Effective sentiment and spam classification on Twitter data

Visualization aids in identifying key features and improving performance

Ablation studies confirm the contribution of each component

Abstract

The successful application of large pre-trained models such as BERT in natural language processing has attracted more attention from researchers. Since the BERT typically acts as an end-to-end black box, classification systems based on it usually have difficulty in interpretation and low robustness. This paper proposes a visual interpretation-based self-improving classification model with a combination of virtual adversarial training (VAT) and BERT models to address the above problems. Specifically, a fine-tuned BERT model is used as a classifier to classify the sentiment of the text. Then, the predicted sentiment classification labels are used as part of the input of another BERT for spam classification via a semi-supervised training manner using VAT. Additionally, visualization techniques, including visualizing the importance of words and normalizing the attention head matrix, are employed to analyze the relevance of each component to classification accuracy. Moreover, brand-new features will be found in the visual analysis, and classification performance will be improved. Experimental results on Twitter's tweet dataset demonstrate the effectiveness of the proposed model on the classification task. Furthermore, the ablation study results illustrate the effect of different components of the proposed model on the classification results.