mlsanityData to 7 Oct 2026

Advances in Multi-turn Dialogue Comprehension: A Survey

arXiv 2103.03125 ↗First posted 4 Mar 2021cs.CL cs.AI cs.HCcited by 12

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Abstract (arXiv)

Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence. A diversity of dialogue systems has been designed with the rapid development of deep learning techniques, especially the recent pre-trained language models (PrLMs). Among these studies, the fundamental yet challenging type of task is dialogue comprehension whose role is to teach the machines to read and comprehend the dialogue context before responding. In this paper, we review the previous methods from the technical perspective of dialogue modeling for the dialogue comprehension task. We summarize the characteristics and challenges of dialogue comprehension in contrast to plain-text reading comprehension. Then, we discuss three typical patterns of dialogue modeling. In addition, we categorize dialogue-related pre-training techniques which are employed to enhance PrLMs in dialogue scenarios. Finally, we highlight the technical advances in recent years and point out the lessons from the empirical analysis and the prospects towards a new frontier of researches.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 69 of its 118 references that are in the map. First posted on arXiv 4 Mar 2021.

Builds on

69 of 118 references.

References · 69

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38 more
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50 most cited of 69 All 69 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 2; 2024: 3; 2025: 0; 2026: 1.

12 papers.

  1. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  2. 2021Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehensioncited by 13
  3. 2021Dialogue-oriented Pre-trainingcited by 9
  4. 2024What's under the hood: Investigating Automatic Metrics on Meeting Summarizationcited by 9
  5. 2024M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in Large Language Modelscited by 7
  6. 2024Navigating the Landscape of Hint Generation Research: From the Past to the Futurecited by 6
  7. 2023Multi-turn Dialogue Comprehension from a Topic-aware Perspectivecited by 4
  8. 2022Utterance Rewriting with Contrastive Learning in Multi-turn Dialoguecited by 1
  9. 2022DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selectioncited by 1
  10. 2026MedAidDialog: A Multilingual Multi-Turn Medical Dialogue Dataset for Accessible Healthcarecited by 1
  11. 2022Sparse and Dense Approaches for the Full-rank Retrieval of Responses for Dialoguescited by 0
  12. 2022STRUDEL: Structured Dialogue Summarization for Dialogue Comprehensioncited by 0