Study Reveals Context Saturation and Discourse Patterns in Emotion Recognition from Conversation
Researchers systematically investigated Emotion Recognition in Conversation (ERC) models, finding that conversational context dominates performance but saturates within 10–30 preceding turns, and that sad utterances show distinct discourse-marker patterns. The study used controlled ablations on the IEMOCAP dataset with cross-validation on MELD, applying rigorous statistical testing across 10 random seeds. The findings suggest competitive emotion recognition is achievable without future context, and that sadness may be uniquely dependent on conversational history rather than local linguistic cues.
A preprint posted to arXiv investigates two underexplored questions in Emotion Recognition in Conversation (ERC): which modeling choices actually drive performance, and how recognition results connect to interpretable linguistic patterns. Using controlled ablations on the IEMOCAP benchmark with cross-dataset validation on MELD, the authors find that conversational context is the single most important factor, but roughly 90% of its benefit is captured within the 10–30 most recent turns. Hierarchical sentence representations help in utterance-only settings and on MELD, but their advantage disappears once turn-level context is included, implying that conversational history subsumes intra-utterance structure. Integrating an external affective lexicon provided no improvement, consistent with pretrained language encoders already encoding most affective signal. Under a strictly causal (no future-turn) setting, the models achieved 82.69% weighted F1 on a 4-way classification and 67.07% on a 6-way task. A linguistic analysis of 5,286 discourse-marker occurrences found a statistically significant association between emotion category and marker position, with sad utterances showing notably reduced left-periphery marker usage (21.9% vs. 28–32% for other emotions), aligning with the finding that sadness benefits most from additional conversational context.
What's missing
The study relies on two specific English-language conversational datasets (IEMOCAP and MELD); generalizability to other languages, domains, or spontaneous real-world conversation remains untested. The paper does not address potential annotation subjectivity in emotion labeling or how inter-annotator agreement may affect the reported performance ceilings. It is also unclear whether the discourse-marker findings replicate in non-scripted or non-English corpora.
What different sources said
- arXiv cs.AICenter
Causal Emotion Recognition in Conversation: Context Saturation and Discourse-Marker Evidence
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