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GSN: A Graph-Structured Network for Multi-Party Dialogues

arXiv 1905.13637 ↗First posted 31 May 2019cs.CL cs.LGcited by 26

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Abstract (arXiv)

Existing neural models for dialogue response generation assume that utterances are sequentially organized. However, many real-world dialogues involve multiple interlocutors (i.e., multi-party dialogues), where the assumption does not hold as utterances from different interlocutors can occur "in parallel." This paper generalizes existing sequence-based models to a Graph-Structured neural Network (GSN) for dialogue modeling. The core of GSN is a graph-based encoder that can model the information flow along the graph-structured dialogues (two-party sequential dialogues are a special case). Experimental results show that GSN significantly outperforms existing sequence-based models.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 May 2019.

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Citing papers per year: 2021: 8; 2022: 1; 2023: 8; 2024: 4; 2025: 4; 2026: 1.

26 papers.

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  1. 2024Hello Again! LLM-powered Personalized Agent for Long-term Dialoguecited by 121
  2. 2021MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understandingcited by 36
  3. 2021DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehensioncited by 16
  4. 2022HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversationscited by 16
  5. 2021Schema-Guided Paradigm for Zero-Shot Dialogcited by 13
  6. 2021Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehensioncited by 13
  7. 2021Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehensioncited by 13
  8. 2023Multi-party Goal Tracking with LLMs: Comparing Pre-training, Fine-tuning, and Prompt Engineeringcited by 13
  9. 2023Is ChatGPT a Good Multi-Party Conversation Solver?cited by 13
  10. 2021Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialoguescited by 11
  11. 2023GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understandingcited by 10
  12. 2024Do LLMs suffer from Multi-Party Hangover? A Diagnostic Approach to Addressee Recognition and Response Selection in Conversationscited by 9
14 more
  1. 2024Friends-MMC: A Dataset for Multi-modal Multi-party Conversation Understandingcited by 9
  2. 2021Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversationscited by 8
  3. 2023EM Pre-training for Multi-party Dialogue Response Generationcited by 7
  4. 2021Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networkscited by 5
  5. 2023MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generationcited by 5
  6. 2023Pre-training Multi-party Dialogue Models with Latent Discourse Inferencecited by 4
  7. 2023P-Tensors: a General Formalism for Constructing Higher Order Message Passing Networkscited by 2
  8. 2025Stick to Facts: Towards Fidelity-oriented Product Description Generationcited by 2
  9. 2023Generic Dependency Modeling for Multi-Party Conversationcited by 1
  10. 2025Advancing Multi-Party Dialogue Framework with Speaker-ware Contrastive Learningcited by 1
  11. 2025SS-MPC: A Sequence-Structured Multi-Party Conversation Systemcited by 1
  12. 2024Multi-party Response Generation with Relation Disentanglementcited by 0
  13. 2025Can MLLMs Generalize to Multi-Party dialog? Exploring Multilingual Response Generation in Complex Scenarioscited by 0
  14. 2026Discourse Coherence and Response-Guided Context Rewriting for Multi-Party Dialogue Generationcited by 0