# Assessment of Parent-Child Interaction Quality from Dyadic Dialogue
## 1 Introduction  
Although the quality of parent-child interaction (PCI) profoundly impacts a child's cognitive and socio-emotional development, PCI can be a challenging issue [1, 2]. Therefore, parent-child interaction therapy (PCIT), a therapeutic approach, is crafted to assist parents of children experiencing early behavior problems in enhancing their relationship with their child and effectively managing their child's behavior [3]. PCIT is linked to favorable outcomes for both children and families, leading to a decrease in child behavior problems and alleviation of family stress [4, 5]. The Dyadic Parent-Child Interaction Coding System (DPICS) was developed in tandem with PCIT to monitor treatment progress. The DPICS allows for the quantification of child and parent behaviors in dyadic interaction, and DPICS has been extensively employed in the assessment of parent-child interaction quality and treatment outcomes. The DPICS is typically coded manually by a trained therapist or research staff [6]. This can be problematic, as time spent training to code to fidelity is costly. Additionally, if large amounts of data are being collected, time spent coding can delay the research process significantly. Artificial intelligence is an emerging trend propelled by the swift advancement of machine learning and deep learning technologies. The goal of artificial intelligence isto create intelligent agents that are capable of completing tasks in a manner similar to humans. The state-of-the-art results and superhuman achievements have been attained in many fields, including AlphaGo in the Go game, Atlas of Boston Dynamics in whole-body robots, and recent conversational dialogue agent ChatGPT. Within the realm of natural language processing, pre-trained autoregressive deep learning language models like BERT and GPT have gained growing popularity [7; 8]. Giving computers the ability to understand human language has long been a goal of artificial intelligence in natural language processing, and pre-trained models are fed massive raw documents in the hopes of identifying relationships among words or sentences. Labeling DPICS codes is a laborious and time-consuming task for both experts and therapists. To assist PCIT therapists, [PERSON] et al. introduced the SpecialTime system, designed to offer parents feedback as they engage in at-home practice of PCIT skills [9]. The developed SpecialTime system can automatically classify child-directed dialogue acts into the eight DPICS classes. Based on the SpecialTime system, we developed and implemented sentence-based classifiers to improve the DPICS code classification results and extend eight DPICS code classes to ten DPICS code classes which are Unlabeled Praise (UP), Labeled Praise (LP), Reflection (RF), Behavior Description (BD), Information Question (IQ), Descriptive Question (DQ), Indirect Commands (IC), Direct Commands (DC), Negative Talk (NTA), and Neutral Talk (TA) following the instruments of the Dyadic Parent-Child Interaction Coding System Comprehensive Manual for Research and Training, Fourth Edition (DPICS-IV) [6]. To do that, we first collected three section datasets including 1753 instances provided by DPICS-IV manual, a total of 1952 utterances from five families, and a PCIT dataset containing 6021 utterances provided by [9]. For comparison, we also deploy typical and popular text feature extraction methods like Word of Bag, Term Frequency-Inverse Document Frequency, and Global Vectors for Word Representation. Additionally, machine learning approaches such as logistic regression, support vector machines, and XGBoost tree were compared as downstream classifiers. In terms of pre-trained deep learning models, we utilized and fine-tuned two variants of BERT: DistilBERT and RoBERTa. After comparing different text representations and machine learning methods, we found that RoBERTa outperformed other methods on our datasets. In particular, RoBERTa's performance surpassed the previous best results on the public PCIT dataset.