Focus on the Core: Efficient Attention via Pruned Token Compression for Document Classification

TL;DR

This paper introduces a novel method combining token pruning and fuzzy logic-based token combining to improve efficiency and accuracy in transformer-based document classification, significantly reducing computational costs while boosting performance.

Contribution

It presents a new integrated approach for token pruning and combining with fuzzy logic to enhance transformer efficiency and accuracy in document classification tasks.

Findings

Achieved +5% accuracy over baseline BERT.

Reduced memory cost to 0.61x of original.

Speeded up processing by 1.64x.

Abstract

Transformer-based models have achieved dominant performance in numerous NLP tasks. Despite their remarkable successes, pre-trained transformers such as BERT suffer from a computationally expensive self-attention mechanism that interacts with all tokens, including the ones unfavorable to classification performance. To overcome these challenges, we propose integrating two strategies: token pruning and token combining. Token pruning eliminates less important tokens in the attention mechanism's key and value as they pass through the layers. Additionally, we adopt fuzzy logic to handle uncertainty and alleviate potential mispruning risks arising from an imbalanced distribution of each token's importance. Token combining, on the other hand, condenses input sequences into smaller sizes in order to further compress the model. By integrating these two approaches, we not only improve the model's performance but also reduce its computational demands. Experiments with various datasets demonstrate superior performance compared to baseline models, especially with the best improvement over the existing BERT model, achieving +5%p in accuracy and +5.6%p in F1 score. Additionally, memory cost is reduced to 0.61x, and a speedup of 1.64x is achieved.