TOWARDS AN ROBUST AND UNIVERSAL SEMANTIC REPRESENTATION FOR ACTION DESCRIPTION

Achieving an robust and universal semantic representation for action description remains the key challenge in natural language understanding. Current approaches often struggle to capture the subtlety of human actions, leading to limited representations. To address this challenge, we propose a novel framework that leverages deep learning techniques