An Interaction-process-guided Framework for Small-group Performance Prediction
Journal
ACM Transactions on Multimedia Computing, Communications and Applications
Journal Volume
19
Journal Issue
2
Pages
1–25
Date Issued
2023-02-06
Author(s)
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 end-to-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's 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 National Taiwan University Business Administration (NTUBA) 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.
Subjects
communicative functions | multimodal behaviors | Small group interaction | Supervised Auto-encoder
SDGs
Publisher
Association for Computing Machinery
Type
journal article