# Assessment of Parent-Child Interaction Quality from Dyadic Dialogue
## 5 Results
### Transfer Learning  
In this section, we applied our trained DPICS models to classify parent-child interaction in real-life scenarios. The DPICS model was trained on the full DPICS-IV dataset. Then,  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy**} & **Macro-Precision** & **Macro-Recall** & **Macro-F** \\ \hline BoW & LR & \(52.312\pm 3.801\) & \(52.619\pm 4.237\) & \(46.632\pm 2.676\) & \(47.476\pm 3.632\) \\  & SVM & \(48.715\pm 1.986\) & \(55.980\pm 4.960\) & \(39.626\pm 3.020\) & \(41.701\pm 3.613\) \\  & XGBoost & \(53.388\pm 1.067\) & \(52.455\pm 2.327\) & \(47.847\pm 1.296\) & \(48.574\pm 1.072\) \\ TF-IDF & LR & \(47.975\pm 2.379\) & \(53.529\pm 4.517\) & \(38.669\pm 0.765\) & \(40.430\pm 1.292\) \\  & SVM & \(47.976\pm 2.258\) & \(56.663\pm 5.859\) & \(37.642\pm 2.014\) & \(40.103\pm 2.747\) \\  & XGBoost & \(50.942\pm 1.253\) & \(49.655\pm 2.977\) & \(45.314\pm 1.448\) & \(46.088\pm 1.479\) \\ \hline  & LR & \(47.294\pm 3.745\) & \(46.203\pm 4.628\) & \(44.000\pm 3.973\) & \(44.171\pm 4.016\) \\ GloVe & SVM & \(42.727\pm 0.318\) & \(54.213\pm 4.074\) & \(32.556\pm 0.876\) & \(35.368\pm 1.259\) \\  & XGBoost & \(52.201\pm 3.495\) & \(50.315\pm 5.158\) & \(46.711\pm 3.005\) & \(47.026\pm 3.946\) \\ \hline DistilBERT & Fine-tune & \(69.426\pm 5.657\) & \(69.346\pm 4.219\) & \(66.248\pm 3.619\) & \(65.543\pm 4.571\) \\ RoBERTa & Fine-tune & \(72.337\pm 3.559\) & \(72.284\pm 3.929\) & \(70.502\pm 2.642\) & \(69.617\pm 3.769\) \\ \hline \hline \end{tabular}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy**} & **Macro-Precision** & **Macro-Recall** & **Macro-F** \\ \hline BoW & LR & \(52.312\pm 3.801\) & \(52.619\pm 4.237\) & \(46.632\pm 2.676\) & \(47.476\pm 3.632\) \\  & SVM & \(48.715\pm 1.986\) & \(55.980\pm 4.960\) & \(39.626\pm 3.020\) & \(4  
our collected five-family dataset was evaluated. This step is important as an application to real-life data allows us to see how well our models perform outside of the controlled environment of the training and testing datasets. The results of our real-life classification are presented in Table 6, where we report overall accuracy as the evaluation metric. This metric is intuitive and straightforward, as overall accuracy simply measures the proportion of correct classifications out of all classifications made.  
According to the results presented in Table 6, RoBERTa achieved the highest accuracy, with an average accuracy of 71.0% when applied to the five family datasets, while DistilBERT also demonstrated successful transfer learning. However, both BoW and TF-IDF did not perform well in transfer learning. Despite being widely used and recommended in previous work [19; 20; 22], these methods require a complete and adequate corpus to produce accurate results. Our dataset was limited and expert-annotated data was expensive, which likely contributed to their poor performance. The GloVe method, which is word-based, performed better than BoW and TF-IDF but did not surpass the accuracy of DistilBERT and RoBERTa.  
The results presented in our study demonstrate that transfer learning is applicable and can achieve acceptable results in DPICS code classification. The primary goal of developing DPICS classifiers is to assist and speed up the labeling process for experts. As such, it is important to consider the few-shot task in this development process. By leveraging the pre-trained models and fine-tuning them on small labeled datasets, we can efficiently classify parent-child interactions with a limited amount of annotated data.