# Improving the Generalizability of Collaborative Dialogue Analysis with Multi-Feature Embeddings

arXiv: https://arxiv.org/abs/2302.04716 · HTML: https://mlsanity.com/paper/2302.04716/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Learning a Universal Template for Few-shot Dataset Generalization](https://mlsanity.com/paper/2105.07029/) (2021)
- [Optimized Generic Feature Learning for Few-shot Classification across Domains](https://mlsanity.com/paper/2001.07926/) (2020)
- [Analyzing Verbal and Nonverbal Features for Predicting Group Performance](https://mlsanity.com/paper/1907.01369/) (2019)
- [Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop](https://mlsanity.com/paper/1804.02063/) (2018)
- [Analyzing Team Performance with Embeddings from Multiparty Dialogues](https://mlsanity.com/paper/2101.09421/) (2021)
- [A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue Comments](https://mlsanity.com/paper/2011.04867/) (2020)

## Built on by (most cited first, 1 of 1)

- [Automatic Analysis of Collaboration Through Human Conversational Data Resources: A Review](https://mlsanity.com/paper/2603.19292/) (2026)

## Abstract (arXiv)

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.