Automated transcription and segmentation achieve CPS detection accuracy comparable to manual pipelines on the Weights Task Dataset, but reduce utterance count by 26.5% and granularity.
Any Other Thoughts, Hedgehog? Linking Deliberation Chains in Collaborative Dialogues
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
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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2025 1verdicts
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Dude, where's my utterance? Evaluating the effects of automatic segmentation and transcription on CPS detection
Automated transcription and segmentation achieve CPS detection accuracy comparable to manual pipelines on the Weights Task Dataset, but reduce utterance count by 26.5% and granularity.