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Automatic detection of behavioural codes in team interactions
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AI
DOI：10.1016/j.csl.2021.101339.png)
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Abstract
En 中文
This paper investigates the feasibility of the task of automatic behaviour coding of spoken interactions in teamwork settings. We introduce the coding schema used to classify the behaviours of the group members and the corpus we collected to assess the coding schema reliability in real teamwork meetings. The behaviours embedded in spoken utterances are modelled using a discriminative approach based on conditional random fields, and state-of-theart neural networks based models. Moreover, we fine-tune publicly available language models to fit our target domain and task and demonstrate how this type of knowledge transfer improves classification models' generalisation capacity. To utilise public resources, the AMI corpus was used for deploying the proposed framework. However, the models were evaluated on both AMI (matched task) and recordings of students solving an engineering challenge (mismatched task). Evaluation results reveal that neural networks are the best performing models in matched tasks, but that CRF models outperform them in mismatched tasks. Mitigating the effect of noisy data, by implementing a lightly supervised approach leads to relative improvements of 32% and 22%, in F1 measures of CRF and BERT, respectively. The proposed classifiers are used as a part of technological support to the training programme in collaborative skills for undergraduate students.
Keywords:
Behaviour codes recognition
Teamwork
Small group interaction
Spoken discourse understanding
CRF
RNN
Journal
C
IF:
3.4
Papers:
1.5K
Citations:
2.6K