Continuous Adversarial Text Representation Learning for Affective Recognition
This addresses the need for better emotion-aware embeddings in affective recognition tasks, though it appears incremental as it builds on existing transformer and contrastive learning methods.
The paper tackles the problem of transformer-based language models struggling to capture nuanced affective information for emotion recognition by proposing a framework that uses continuous valence-arousal labeling and dynamic token perturbation. The result is a 15.5% improvement in emotion classification benchmarks.
While pre-trained language models excel at semantic understanding, they often struggle to capture nuanced affective information critical for affective recognition tasks. To address these limitations, we propose a novel framework for enhancing emotion-aware embeddings in transformer-based models. Our approach introduces a continuous valence-arousal labeling system to guide contrastive learning, which captures subtle and multi-dimensional emotional nuances more effectively. Furthermore, we employ a dynamic token perturbation mechanism, using gradient-based saliency to focus on sentiment-relevant tokens, improving model sensitivity to emotional cues. The experimental results demonstrate that the proposed framework outperforms existing methods, achieving up to 15.5% improvement in the emotion classification benchmark, highlighting the importance of employing continuous labels. This improvement demonstrates that the proposed framework is effective in affective representation learning and enables precise and contextually relevant emotional understanding.