mlsanityData to 7 Oct 2026

ORCHID: A Chinese Debate Corpus for Target-Independent Stance Detection and Argumentative Dialogue Summarization

arXiv 2410.13667 ↗First posted 17 Oct 2024cs.CLcited by 6

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Abstract (arXiv)

Dialogue agents have been receiving increasing attention for years, and this trend has been further boosted by the recent progress of large language models (LLMs). Stance detection and dialogue summarization are two core tasks of dialogue agents in application scenarios that involve argumentative dialogues. However, research on these tasks is limited by the insufficiency of public datasets, especially for non-English languages. To address this language resource gap in Chinese, we present ORCHID (Oral Chinese Debate), the first Chinese dataset for benchmarking target-independent stance detection and debate summarization. Our dataset consists of 1,218 real-world debates that were conducted in Chinese on 476 unique topics, containing 2,436 stance-specific summaries and 14,133 fully annotated utterances. Besides providing a versatile testbed for future research, we also conduct an empirical study on the dataset and propose an integrated task. The results show the challenging nature of the dataset and suggest a potential of incorporating stance detection in summarization for argumentative dialogue.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 34 of its 76 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

34 of 76 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

  1. 2025Large Language Models in Argument Mining: A Surveycited by 14
  2. 2025Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directionscited by 5
  3. 2025R-Debater: Retrieval-Augmented Debate Generation through Argumentative Memorycited by 4
  4. 2025LASTIST: LArge-Scale Target-Independent STance datasetcited by 0
  5. 2025Winning with Less for Low Resource Languages: Advantage of Cross-Lingual English_Persian Argument Mining Model over LLM Augmentationcited by 0
  6. 2026Chinese Competitive Debating Dataset and Benchmarkcited by 0