Conflict prediction in communication is integral to the design of virtual
agents that support successful teamwork by providing timely assistance. The aim
of our research is to analyze discourse to predict collaboration success.
Unfortunately, resource scarcity is a problem that teamwork researchers
commonly face since it is hard to gather a large number of training examples.
To alleviate this problem, this paper introduces a multi-feature embedding
(MFeEmb) that improves the generalizability of conflict prediction models
trained on dialogue sequences. MFeEmb leverages textual, structural, and
semantic information from the dialogues by incorporating lexical, dialogue
acts, and sentiment features. The use of dialogue acts and sentiment features
reduces performance loss from natural distribution shifts caused mainly by
changes in vocabulary.
This paper demonstrates the performance of MFeEmb on domain adaptation
problems in which the model is trained on discourse from one task domain and
applied to predict team performance in a different domain. The generalizability
of MFeEmb is quantified using the similarity measure proposed by Bontonou et
al. (2021). Our results show that MFeEmb serves as an excellent domain-agnostic
representation for meta-pretraining a few-shot model on collaborative
multiparty dialogues.

本研究旨在通过分析话语以预测协作成功，引入了一种多特征嵌入方法（MFeEmb），它结合了文本、结构和语义信息，包括词汇、对话和情感特征，通过使用对话行为和情感特征降低自然分布变化带来的性能损失，并在不同领域的团队表现中展示了 MFeEmb 的泛化性能。