Implicit Proposal Filtering in Multi-Party Consensus-Building Conversations
نویسندگان
چکیده
An attempt was made to statistically estimate proposals which survived the discussion to be incorporated in the final agreement in an instance of a Japanese design conversation. Low level speech and vision features of hearer behaviors corresponding to aiduti, noddings and gaze were found to be a positive predictor of survival. The result suggests that non-linguistic hearer responses work as implicit proposal filters in consensus building, and could provide promising candidate features for the purpose of recognition and summarization of meeting events.
منابع مشابه
Computing Backchannel Distributions in Multi-Party Conversations
In multi-party conversations it may not always be obvious who is talking to whom. Backchannels may provide a partial answer to this question, possibly in combination with some other events, such as gaze behaviors of the interlocutors. We look at some patterns in multi-party interaction relating features of backchannel behaviours to aspects of the partipation framework.
متن کاملAiduti in Japanese Multi-Party Design Conversations
Japanese backchannel utterances, aizuti, in a multi-party design conversation were examined, and aizuti functions were analyzed in comparison with its functions in twoparty dialogues. In addition to the two major functions, signaling acknowledgment and turn-management, it was argued that aizuti in multi-party conversations are involved in joint construction of design plans through management of...
متن کاملOn Transaction Pseudonyms with Implicit Attributes
Transaction pseudonyms with implicit attributes are a novel approach to multilevel linkable transaction pseudonyms. We extend earlier work of Juels and Pappu on reencryption-based transaction pseudonyms, by developing new mechanisms for controlled pseudonym linkability. This includes mechanisms for cooperative, stepwise re-identification as well as individual authentication of pseudonyms. Our p...
متن کاملArgviz: Interactive Visualization of Topic Dynamics in Multi-party Conversations
We introduce an efficient, interactive framework—Argviz—for experts to analyze the dynamic topical structure of multi-party conversations. Users inject their needs, expertise, and insights into models via iterative topic refinement. The refined topics feed into a segmentation model, whose outputs are shown to users via multiple coordinated views.
متن کاملLinguistic Adaptation in Conversation Threads: Analyzing Alignment in Online Health Communities
Previous studies of alignment have focused on two-party conversations. We study multi-party conversation in online health communities, which have shown benefits for their members from forum conversations. So far, our understanding of the relationship between alignment in such multi-party conversations and its possible connection to member benefits has been limited. This paper quantifies linguis...
متن کامل