Papers › Directed Acyclic Graph Network for Conversational Emotion Recognition
Directed Acyclic Graph Network for Conversational Emotion Recognition
Weizhou Shen, Siyue Wu, Yunyi Yang, Xiaojun Quan
The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Emotion Recognition in Conversation | DailyDialog | DAG-ERC | Micro-F1 | 59.33 | #10 of 22 | Archive leaderboard | report |
| Emotion Recognition in Conversation | EmoryNLP | DAG-ERC | Weighted-F1 | 39.02 | #14 of 28 | Archive leaderboard | report |
| Emotion Recognition in Conversation | IEMOCAP | DAG-ERC | Weighted-F1 | 68.03 | #29 of 59 | Archive leaderboard | report |
| Emotion Recognition in Conversation | MELD | DAG-ERC | Weighted-F1 | 63.65 | #42 of 68 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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