NTDH: Complex Reasoning for Comprehensive Affective Analysis
Comprehensive affective analysis is challenging for two reasons: it spans heterogeneous prediction tasks with continuous, ordinal, and multi-label outputs, and affective meaning is context-dependent, requiring conflicting cues to be reconciled rather than mapped directly to labels. Existing methods learn this mapping directly and do not model the reconciliation explicitly. We recast the task as a complex-reasoning problem, which yields one output interface across heterogeneous label spaces and a trajectory over which a verifiable reward can be optimised; to our knowledge, this is the first such treatment covering both sentiment and emotion. The obstacle is on the data side: affective reas...
| Authors | Tianlei Zhu, Zhiwei Liu, Yuyan Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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