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An Interaction Process Guided Framework for Small-Group Performance Prediction
Abstract
A small group is a fundamental interaction unit for achieving a shared goal. Group performance can be automatically predicted using computational methods to analyze members' verbal behavior in task-oriented interactions, as has been proven in several recent works. Most of the prior works focus on lower-level verbal behaviors, such as acoustics and turn-taking patterns, using either hand-crafted features or even advanced endto-end methods. However, higher-level group-based communicative functions used between group members during conversations have not yet been considered. In this work, we propose a two-stage training framework that effectively integrates the communication function, as defined using Bales' interaction process analysis (IPA) coding system, with the embedding learned from the low-level features in order to improve the group performance prediction. Our result shows a significant improvement compared to the state-of-the-art methods (4.241 MSE and 0.341 Pearson's correlation on NTUBA-task1 and 3.794 MSE and 0.291 Pearson's correlation on NTUBA-task2) on the NTUBA (National Taiwan University Business Administration) small-group interaction database. Furthermore, based on the design of IPA, our computational framework can provide a time-grained analysis of the group communication process and interpret the beneficial communicative behaviors for achieving better group performance.
Figures
Our Proposed Computational Framework with Supervised Interaction Process Auto-encoder: the proposed framework includes 2-staged training.
Our Proposed Computational Framework with Supervised Interaction Process Auto-encoder: the proposed framework includes 2-staged training.
Keywords
small group interaction | supervised Auto-encoder | communicative functions | multimodal behaviors
Authors
Chi-Chun Lee
Publication Date
2022/11/01
Journal
ACM TOMM
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)
Publisher
ACM