Directed Acyclic Graph Network for Conversational Emotion Recognition (2021.acl-long)
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| Challenge: | Empirical evidence shows that a good representation of conversation context significantly contributes to the model performance. |
| Approach: | They propose to encode query utterances with a directed acyclic graph to better model the intrinsic structure within a conversation. |
| Outcome: | The proposed model outperforms existing models on four ERC benchmarks with state-of-the-art models employed as baselines. |
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| Challenge: | Emotion recognition in conversation (ERC) has received much attention lately due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. |
| Approach: | They propose a graph neural network-based approach to emotion recognition in conversation that leverages self and inter-speaker dependency of the interlocutors to model conversational context. |
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MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation (2022.lrec-1)
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| Challenge: | Emotion recognition in conversation is important for an empathetic dialogue system to understand the user’s emotion and then generate appropriate emotional responses. |
| Approach: | They propose to use multimodal directed acyclic graphs to integrate multimodal information and contextual information into a DAG architecture to exploit multimodal contexts. |
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DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations (2021.acl-long)
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| Challenge: | Recent studies on ERC lack the ability to extract and integrate emotional clues from the conversational context. |
| Approach: | They propose a new model that uses multi-turn reasoning modules to extract and integrate emotional clues from conversational context. |
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S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation (2022.aacl-main)
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| Challenge: | Emotion recognition in conversation (ERC) is a task arousing increasing interest in many fields. |
| Approach: | They propose a novel GNN-based ERC model that captures speaker and position information. |
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Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition (2024.lrec-main)
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| Challenge: | Existing models for multimodal Emotion Recognition in conversation (ERC) use text as the main modality for emotion recognition. |
| Approach: | They propose a Directed Acyclic Graph (DAG) approach that integrates textual, acoustic, and visual features within a unified framework. |
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Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations (2020.emnlp-main)
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| Challenge: | Recent research on emotion recognition in conversations (ERC) does not take self-dependency or inter-speaker dependency into account. |
| Approach: | They propose a relational graph attention network (RGAT) model that takes speaker dependency and sequential information into account by encoding the relational Graph structure. |
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An Iterative Emotion Interaction Network for Emotion Recognition in Conversations (2020.coling-main)
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| Challenge: | Emotion recognition in conversations (ERC) is a task that aims to recognize the emotion of each utterance in conversations. |
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ESCP: Enhancing Emotion Recognition in Conversation with Speech and Contextual Prefixes (2024.lrec-main)
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| Challenge: | Emotion Recognition in Conversation (ERC) aims to analyze the speaker’s emotional state in a conversation. |
| Approach: | They propose to combine a directed acyclic graph and contextual prefixes to model historical utterances in a conversation and incorporate a contextual prefixed containing sentiment and semantics of historical . |
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A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation (2021.findings-emnlp)
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| Challenge: | Prior research focuses on exploring sequential information but ignores the discourse structures of conversations. |
| Approach: | They propose a discourse-aware graph neural network (ERMC-DisGCN) that leverages contextual cues and speaker-specific features for ERMC. |
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Enhancing Emotion Recognition in Conversation via Multi-view Feature Alignment and Memorization (2023.findings-emnlp)
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| Challenge: | Emotion recognition in conversation (ERC) is an advanced capability of conversational AI systems. |
| Approach: | They propose a semi-parametric paradigm for Emotion Recognition in conversation that uses supervised contrastive learning to align semantic-view and context-view features. |
| Outcome: | The proposed model achieves state-of-the-art on four widely used benchmarks. |