mlsanityData to 14 Oct 2026

NewsDialogues: Towards Proactive News Grounded Conversation

arXiv 2308.06501 ↗First posted 12 Aug 2023cs.CLcited by 5

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Abstract (arXiv)

Hot news is one of the most popular topics in daily conversations. However, news grounded conversation has long been stymied by the lack of well-designed task definition and scarce data. In this paper, we propose a novel task, Proactive News Grounded Conversation, in which a dialogue system can proactively lead the conversation based on some key topics of the news. In addition, both information-seeking and chit-chat scenarios are included realistically, where the user may ask a series of questions about the news details or express their opinions and be eager to chat. To further develop this novel task, we collect a human-to-human Chinese dialogue dataset tsNewsDialogues, which includes 1K conversations with a total of 14.6K utterances and detailed annotations for target topics and knowledge spans. Furthermore, we propose a method named Predict-Generate-Rank, consisting of a generator for grounded knowledge prediction and response generation, and a ranker for the ranking of multiple responses to alleviate the exposure bias. We conduct comprehensive experiments to demonstrate the effectiveness of the proposed method and further present several key findings and challenges to prompt future research.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 38 of its 55 references that are in the map. First posted on arXiv 12 Aug 2023.

Builds on

38 of 55 references.

References · 38

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Built on by

Cited by year

Citing papers per year: 2024: 4; 2025: 1.

5 papers.

  1. 2024A Survey on Recent Advances in Conversational Data Generationcited by 23
  2. 2024Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcriptscited by 6
  3. 2024"It Might be Technically Impressive, But It's Practically Useless to us": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industrycited by 5
  4. 2024An Energy-based Model for Word-level AutoCompletion in Computer-aided Translationcited by 0
  5. 2025Proactive User Information Acquisition via Chats on User-Favored Topicscited by 0