REFLECT: Role-Aware Detection of Sentiment, Emotion, Politeness, and Formality

Key points

• Existing approaches rely on contextual language representations while leaving semantic roles implicit.1
• We introduce a role-aware transformer architecture that maps the four SEPF dimensions to theory-grounded Semantic Role Labeling (SRL) configurations, incorporating these configurations directly into the transformer's self-attention mechanism.1
• Role-aware attention models non-directional internal states (sentiment and emotion), while directional role-aware attention captures initiator-to-recipient relations for external cues (politeness and formality).1
• These results demonstrate that linguistically grounded modifications to transformer self-attention improve the detection of communicative meaning, generalize beyond benchmark datasets to real-world mental-health data, and yield traceable, role-grounded explanations that extend beyond predictive performance to interpretability.1

Sources (1)

• [1]REFLECT: Role-Aware Detection of Sentiment, Emotion, Politeness, and Formality
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 04:10 PM

  Detecting communicative meaning in dialogue requires modeling not only lexical content but also the participant-role structures through which sentiment, emotion, politeness, and formality (SEPF) are expressed.

  Existing approaches rely on contextual language representations while leaving semantic roles implicit.

Extractive summary: sentences quoted from the sources.