mlsanityData to 7 Oct 2026

DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension

arXiv 2104.12377 ↗First posted 26 Apr 2021cs.CLcited by 16

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Abstract (arXiv)

Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC as models must handle the complex dialogue discourse structure, previously unconsidered in traditional MRC. To fully exploit such discourse structure in multiparty dialogue, we present a discourse-aware dialogue graph neural network, DADgraph, which explicitly constructs the dialogue graph using discourse dependency links and discourse relations. To validate our model, we perform experiments on the Molweni corpus, a large-scale MRC dataset built over multiparty dialogue annotated with discourse structure. Experiments on Molweni show that our discourse-aware model achieves statistically significant improvements compared against strong neural network MRC baselines.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 20 of its 38 references that are in the map. First posted on arXiv 26 Apr 2021.

Builds on

20 of 38 references.

References · 20

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8 more
  1. 2019DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversationcited by 184
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  5. 2020Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structurecited by 60
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Citing papers per year: 2021: 5; 2022: 4; 2023: 5; 2024: 2.

16 papers.

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  1. 2022Let's Negotiate! A Survey of Negotiation Dialogue Systemscited by 31
  2. 2021Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehensioncited by 13
  3. 2021Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehensioncited by 13
  4. 2022Abstractive Meeting Summarization: A Surveycited by 9
  5. 2021Structural Characterization for Dialogue Disentanglementcited by 7
  6. 2022Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modelingcited by 7
  7. 2021Multi-tasking Dialogue Comprehension with Discourse Parsingcited by 6
  8. 2023Multi-Granularity Prompts for Topic Shift Detection in Dialoguecited by 6
  9. 2023Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialoguescited by 4
  10. 2023Topic Shift Detection in Chinese Dialogues: Corpus and Benchmarkcited by 4
  11. 2023Pre-training Multi-party Dialogue Models with Latent Discourse Inferencecited by 4
  12. 2024The Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claimscited by 1
4 more
  1. 2021Advances in Multi-turn Dialogue Comprehension: A Surveycited by 0
  2. 2022Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehensioncited by 0
  3. 2023Coreference-aware Double-channel Attention Network for Multi-party Dialogue Reading Comprehensioncited by 0
  4. 2024Let's Negotiate! A Survey of Negotiation Dialogue Systemscited by 0