TCDA introduces TC-DAG to filter cross-thread noise while preserving temporal order and D-RoPE to align semantics across layers and reduce distance dilution, achieving state-of-the-art results on two DiaASQ benchmarks.
Directed Acyclic Graph Network for Conversational Emotion Recognition
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SCALE disentangles emotion and cause representations in conversations and uses optimal transport for many-to-many global alignment, achieving SOTA on ECPEC benchmarks.
citing papers explorer
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TCDA: Thread-Constrained Discourse-Aware Modeling for Conversational Sentiment Quadruple Analysis
TCDA introduces TC-DAG to filter cross-thread noise while preserving temporal order and D-RoPE to align semantics across layers and reduce distance dilution, achieving state-of-the-art results on two DiaASQ benchmarks.
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Emotion-Cause Pair Extraction in Conversations via Semantic Decoupling and Graph Alignment
SCALE disentangles emotion and cause representations in conversations and uses optimal transport for many-to-many global alignment, achieving SOTA on ECPEC benchmarks.