构建对话中的思考链，揭示提问如何由先前对话引发。
Any Other Thoughts, Hedgehog? Linking Deliberation Chains in Collaborative Dialogues
- 用图模型建模对话中提问的因果链条。
- 在两个任务数据集上表现优于基线与核心指代方法。
- 适合研究协作对话与自然语言推理的学者。
协作对话中的提问是知识建构的关键，尤其在内部和协作式问题解决中。本文聚焦于探询性提问——即明确向对话伙伴寻求回应的问题。我们重点建模对话早期语句如何直接导致探询性提问的产生。提出一种基于图结构的思考链框架，将构建思考链重构为类似共指聚类的问题。该框架联合建模探询性话语、因果语句及其之间的关联关系。我们在两个具有挑战性的协作任务数据集（Weights Task 和 DeliData）上进行评估。结果表明，相比基线方法和更强的核心指代方法，该理论驱动的方法更具有效性，并在这一新任务上建立了性能基准。
原文摘要 · Abstract (English)
Question-asking in collaborative dialogue has long been established as key to knowledge construction, both in internal and collaborative problem solving. In this work, we examine probing questions in collaborative dialogues: questions that explicitly elicit responses from the speaker's interlocutors. Specifically, we focus on modeling the causal relations that lead directly from utterances earlier in the dialogue to the emergence of the probing question. We model these relations using a novel graph-based framework of deliberation chains, and reframe the problem of constructing such chains as a coreference-style clustering problem. Our framework jointly models probing and causal utterances and the links between them, and we evaluate on two challenging collaborative task datasets: the Weights Task and DeliData. Our results demonstrate the effectiveness of our theoretically-grounded approach compared to both baselines and stronger coreference approaches, and establish a standard of performance in this novel task.
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