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Quantitative estimation of the strength of agreements in goal-oriented meetings

机译:面向目标的会议中协议强度的定量估计

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摘要

Ineffective meetings occur frequently and participants leave with different understandings of what has been decided upon. For meetings that require quick responses (e.g., disaster-response planning), everyone must leave the meeting on the same page to ensure the successful execution of the mission. Detecting patterns of weak agreements in planning meetings is the first step towards designing an intelligent agent that encourages team members to revisit decisions that may adversely affect the team's performance, and to spur dialog that results in higher quality plans. This paper presents a statistical approach to learning patterns of strong and weak agreements without using domain-specific content or keywords, meaning the algorithm takes as input information about how the team plans but does not require potentially sensitive data on what is being planned. Our approach applies statistical machine learning to dialog features, which prior studies in cognitive psychology have shown qualitatively capture the level of joint commitment to plan choices. We analyze a real-world conversation dataset, the AMI corpus, to quantitatively verify that dialog features improve the estimation of strength of agreements over prior approaches. We show these results are consistent across a number of different supervised and unsupervised learning algorithms, and that can achieve up to 94% average accuracy in estimating the strength of agreements.
机译:无效的会议经常发生,参与者留下不同的对决定的理解。对于需要快速响应的会议(例如,灾难响应计划),每个人都必须在同一页面上留下会议,以确保成功执行特派团。检测规划会议中的弱协议模式是设计智能代理的第一步,鼓励团队成员重新审视可能对团队绩效产生不利影响的决策,并促使对话的对话导致更高质量的计划。本文提出了一种统计方法,可以在不使用域特定内容或关键字的情况下学习强大和弱趋势的统计方法,这意味着该算法作为对团队计划的输入信息,但不需要对正在计划的内容的潜在敏感数据。我们的方法将统计机器学习应用于对话特征,该特征在于认知心理学的研究表明,定性地捕获了计划选择的共同承诺水平。我们分析了真实世界对话数据集,AMI语料库,以定量验证对话框功能,提高了现有方法的协议强度估计。我们展示这些结果符合许多不同的监督和无人监督的学习算法,并且可以在估计协议的实力方面获得高达94%的平均准确性。

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