BERT meets LIWC: Exploring State-of-the-Art Language Models for Predicting Communication Behavior in Couples' Conflict Interactions
Journal
arXiv.org
Series
Computation and Language (cs.CL)
Type
conference contribution
Date Issued
2021-06-02
Author(s)
Biggiogera, Jacopo
;
Boateng, George
;
Hilpert, Peter
;
Vowels, Matthew
;
Bodenmann, Guy
;
Neysari, Mona
;
Nussbeck, Fridtjof
;
Abstract
Many processes in psychology are complex, such as dyadic interactions between two interacting partners (e.g. patient-therapist, intimate relationship partners). Nevertheless, many basic questions about interactions are difficult to investigate because dyadic processes can be within a person and between partners, they are based on multimodal aspects of behavior and unfold rapidly. Current analyses are mainly based on the behavioral coding method, whereby human coders annotate behavior based on a coding schema. But coding is labor-intensive, expensive, slow, focuses on few modalities. Current approaches in psychology use LIWC for analyzing couples' interactions. However, advances in natural language processing such as BERT could enable the development of systems to potentially automate behavioral coding, which in turn could substantially improve psychological research. In this work, we train machine learning models to automatically predict positive and negative communication behavioral codes of 368 German-speaking Swiss couples during an 8-minute conflict interaction on a fine-grained scale (10-seconds sequences) using linguistic features and paralinguistic features derived with openSMILE. Our results show that both simpler TF-IDF features as well as more complex BERT features performed better than LIWC, and that adding paralinguistic features did not improve the performance. These results suggest it might be time to consider modern alternatives to LIWC, the de facto linguistic features in psychology, for prediction tasks in couples research. This work is a further step towards the automated coding of couples' behavior which could enhance couple research and therapy, and be utilized for other dyadic interactions as well.
Language
English
Keywords
communication behavior
couples
linguistic features
HSG Classification
contribution to scientific community
HSG Profile Area
SoM - Business Innovation
Publisher
arXiv.org
Official URL
Division(s)
Eprints ID
263263
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open.access
Name
Biggiogera et al 2021 Preprint arXiv 2106.01536.pdf
Size
480.33 KB
Format
Adobe PDF
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