Improving the Generalizability of Collaborative Dialogue Analysis with Multi-Feature Embeddings
This work addresses resource scarcity in teamwork research by enhancing conflict prediction models for virtual agents, though it is incremental as it builds on existing embedding and domain adaptation techniques.
The paper tackles the problem of predicting collaboration success from dialogue sequences by introducing a multi-feature embedding (MFeEmb) that improves generalizability across domains, demonstrating its effectiveness as a domain-agnostic representation for meta-pretraining in few-shot settings.
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.