Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention

TL;DR

This paper introduces KEAR, an external attention mechanism that enhances transformer models with external knowledge, achieving human parity on CommonsenseQA by significantly improving reasoning capabilities.

Contribution

It proposes a novel external attention mechanism to augment transformers, enabling better integration of external knowledge for improved commonsense reasoning.

Findings

KEAR reaches 89.4% accuracy on CommonsenseQA

Significant performance improvement over baseline models

Achieves human parity in commonsense reasoning accuracy

Abstract

Most of today's AI systems focus on using self-attention mechanisms and transformer architectures on large amounts of diverse data to achieve impressive performance gains. In this paper, we propose to augment the transformer architecture with an external attention mechanism to bring external knowledge and context to bear. By integrating external information into the prediction process, we hope to reduce the need for ever-larger models and increase the democratization of AI systems. We find that the proposed external attention mechanism can significantly improve the performance of existing AI systems, allowing practitioners to easily customize foundation AI models to many diverse downstream applications. In particular, we focus on the task of Commonsense Reasoning, demonstrating that the proposed external attention mechanism can augment existing transformer models and significantly improve the model's reasoning capabilities. The proposed system, Knowledgeable External Attention for commonsense Reasoning (KEAR), reaches human parity on the open CommonsenseQA research benchmark with an accuracy of 89.4\% in comparison to the human accuracy of 88.9\%.