arXiv AI
Exposing Weaknesses in Emotion Recognition in Conversations
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arXiv AI
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CLEAR: Coupling Local Evidence with Affective Representations for Multimodal Emotion Recognition in Conversation
The paper introduces CLEAR, a framework for Multimodal Emotion Recognition in Conversation that couples contextual representations with local discriminative cues. It replaces emotion label generation with continuous internal representations from a vision‑language model and trains a classifier on these representations. Sample‑adaptive residual coupling is used to integrate textual and acoustic evidence from the target utterance into the contextual representation, improving emotion recognition performance on IEMOCAP and MELD datasets.
arXiv Computation and Language
Aug 28
Evaluating Language Models in Realistic Conversational Contexts
The paper introduces UPHELD, a large benchmark of human-to-human dialogues written by professional script writers, featuring realistic turn densities and over 36,000 per-turn human annotations. It evaluates existing automatic metrics and LLM-as-a-judge methods, finding them unreliable against expert human judgment. Using UPHELD, the authors develop a Mixture-of-Judges framework that improves correlation with human assessments by about 30%.
arXiv AI
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Causal Emotion Recognition in Conversation: Context Saturation and Discourse-Marker Evidence
arXiv:2601. 00181v3 Announce Type: replace-cross Abstract: We address two persistent gaps in Emotion Recognition in Conversation: which modeling choices materially affect performance, and how recognition findings connect to interpretable discourse-level patterns.
arXiv AI
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Beyond Text: LLM-Based Dimensional Emotion Evaluation in Multimodal Dialogue
The paper introduces an LLM-based framework for continuous dimensional emotion evaluation in multimodal dialogue, combining discrete emotion recognition with Valence-Arousal-Dominance (VAD) assessment on the IEMOCAP dataset. It incorporates acoustic cues as natural language descriptions via the SpeechCueLLM approach and evaluates six models from the LLaMA, GPT, and Qwen families using zero-shot, few-shot, and LoRA fine-tuning. LoRA-fine-tuned LLaMA models outperform prompt-engineered GPT models, achieving a new state-of-the-art Valence CCC of 0.7822, and ablation studies show that textual audio descriptions significantly benefit smaller models. "whyItMatters":"The study demonstrates that domain adaptation through fine-tuning can surpass larger GPT models in multimodal emotion evaluation, highlighting the importance of tailored training for emotion recognition tasks."
arXiv Computation and Language
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TalkFa: A Unified Benchmark for Farsi Dialogue Generation and Understanding
TalkFa introduces a unified benchmark for Farsi dialogue generation and understanding, comprising three datasets: WIKI‑FADIAL (4.2K Wikipedia‑grounded dialogues), DAILYDIALOG‑FA (6.6K dialogues with dialogue‑act and emotion annotations), and PLAYDIAL‑FA (2.1K theatrical dialogues with sentiment labels). All dialogues are curated through multi‑stage review by native speakers, ensuring high quality. Experiments show that LoRA fine‑tuning improves generation performance with less data, while specific models excel on classification tasks, and human evaluation confirms the benchmark’s reliability.
arXiv Computation and Language
Sep 15
All Learning Has an Emotional Basis, So Does Task-Oriented Dialogue
arXiv:2507.01594v2 Announce Type: replace Abstract: Task-oriented dialogue (ToD) systems aim to help users accomplish goals through natural language interaction. Beyond task success, effective ToD sy...