Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation
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The success of emotional conversation systems depends on sufficient perception and appropriate expression of emotions. In a real-world conversation, we firstly instinctively perceive emotions from multi-source information, including the emotion flow of dialogue history, facial expressions, and personalities of speakers, and then express suitable emotions according to our personalities, but these multiple types of information are insufficiently exploited in emotional conversation fields. To address this issue, we propose a heterogeneous graph-based model for emotional conversation generation. Specifically, we design a Heterogeneous Graph-Based Encoder to represent the conversation content (i.e., the dialogue history, its emotion flow, facial expressions, and speakers' personalities) with a heterogeneous graph neural network, and then predict suitable emotions for feedback. After that, we employ an Emotion-Personality-Aware Decoder to generate a response not only relevant to the conversation context but also with appropriate emotions, by taking the encoded graph representations, the predicted emotions from the encoder and the personality of the current speaker as inputs. Experimental results show that our model can effectively perceive emotions from multi-source knowledge and generate a satisfactory response, which significantly outperforms previous state-of-the-art models.
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Previous multimodal ERC methods extract facial representations using face detection tools such as MTCNN.
“Some works (Dai et al., 2021; Liang et al., 2021; Hu et al., 2021; Zhao et al., 2022b) also extract facial representations using various tools, such as MTCNN (Zhang et al., 2016).”
From A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations (2023) · cites this paper as Liang et al., 2021
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Exploring the Use of ChatGPT as a Tool for Developing Eportfolios in ESL Classrooms
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Self-citationLeveraging hierarchical semantic‐emotional memory in emotional conversation generation
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DialogCIN: Contextual Inference Networks for Emotional Dialogue Generation
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Dual Learning for Conversational Emotion Recognition and Emotional Response Generation
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RT-KGD: Relation Transition Aware Knowledge-Grounded Dialogue Generation
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Empathetic Response Generation Through Multi-Modality
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Topic-extended Emotional Conversation Generation Model Based on Joint Decoding
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CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AIPreprint
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Self-citationIECCK: integrating emotion cause and common knowledge for empathetic dialogue generation
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Amorin, R. B., & Gallaron, G. M. (2025) · MIER Journal of Educational Studies Trends and Practices · doi:10.52634/mier/2025/v15/i1/2827 · Cited by 3 · see citing works
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GraphMetaP: Efficient MetaPath Generation for Dynamic Heterogeneous Graph Models
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Multimodal Emotion Recognition Using Compressed Graph Neural Networks
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Syntactically Diverse Adversarial Network for Knowledge-Grounded Conversation Generation
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Self-citationModeling Compositionality with Dependency Graph for Dialogue Generation
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Self-citationA study on pharmaceutical text relationship extraction based on heterogeneous graph neural networks
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A Semantic-Enhanced Heterogeneous Dialogue Graph Network for Sentiment Analysis in Conversations
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Hierarchical Planning of Topic-Comment Structure for Paper Abstract Writing
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Dialogue Response Generation for Text-Based Dialogue Systems with Emotion Regulation
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An Answer Recommendation Algorithm Based on Semantic Fusion Heterogeneous Information Network
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A Quality-based Syntactic Template Retriever for Syntactically-Controlled Paraphrase Generation
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Self-citationA Standardized-Based Knowledge Distillation Model for Multimodal Emotion Recognition in ConversationPreprint
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Self-citationGenerating Responses with Emotional Scoring
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MetaHG: Enhancing HGNN Systems Leveraging Advanced Metapath Graph Abstraction
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Enhanced AMR-Based Modeling of Multi-Role Dialogue Dynamics for Abstractive Summarization
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Self-citation
- Authors
- Liang, Y., Meng, F., Zhang, Y., Chen, Y., Xu, J., & Zhou, J.
- Affiliations
- Beijing Jiaotong University (CN)
- Tencent (China) (CN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2021-05-18
- Volume
- 35
- Issue
- 15
- Pages
- 13343-13352
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W3113225429
- Cited by
- 63
- References
- 68
- Citations by year
- 2025: 6 · 2024: 14 · 2023: 19 · 2022: 18 · 2021: 6
- FWCI
- 6.58
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- National Conference on Artificial Intelligence
- Funders
- National Natural Science Foundation of China (61976015, 61876198, 61370130, 61976016)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- heterogeneous graph neural networks, emotion perception, facial expressions, speaker personality, dialogue history
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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