Assessment of Parent–Child Interaction Quality from Dyadic Dialogue

Round 1

Reviewer 1 Report

Clearer Introduction and Motivation:

 

Begin the manuscript with a clear and concise introduction that outlines the importance of parent-child interaction, the challenges in manually coding DPICS, and the need for automated classification using AI.

Clearly state the research objectives and hypotheses that guide the study.

Methodology Section:

 

Provide a more detailed explanation of the DPICS coding system for readers who may not be familiar with it.

Explain the selection process of datasets, including criteria for inclusion and any potential biases.

Describe the pre-processing steps for the data, such as text cleaning, tokenization, and data augmentation, if applicable.

Elaborate on the fine-tuning process for the RoBERTa model, including hyperparameters used and any modifications made to the model architecture.

Experimental Setup:

 

Describe the train-test split methodology and any cross-validation techniques used.

Provide information on the size of the training and testing datasets, as well as any data augmentation techniques applied.

Mention the hardware and software infrastructure used for training and evaluation.

Results Section:

 

Present the results in a clear and organized manner using tables and figures.

Provide statistical significance tests, if applicable, to support the claim that the RoBERTa model outperforms other methods.

Include a discussion of the potential reasons for the observed results, such as the complexity of DPICS codes or the characteristics of the datasets.

Discussion and Implications:

 

Discuss the practical implications of the study's findings. How can automated DPICS code classification benefit parent-child interaction therapists and researchers?

Highlight the limitations of the study, such as dataset size, potential biases, and any challenges encountered during the fine-tuning process.

Offer suggestions for future research in this area, such as exploring the use of larger datasets or different AI models.

Conclusion:

 

Summarize the main findings of the study and their significance.

Restate how the use of the RoBERTa model can assist experts in the labeling process.

End with a strong concluding statement that reinforces the importance of the research.

References:

 

Ensure that all references are properly cited and listed in a consistent citation style (e.g., APA, MLA).

Clarity and Language:

 

Review the manuscript for clarity and coherence, ensuring that the flow of ideas is logical and easy to follow.

Carefully proofread the document for grammar, punctuation, and spelling errors.

Abstract:

 

Write a clear and concise abstract that summarizes the key points of the study, including the problem addressed, methodology, results, and implications.

Clearer Introduction and Motivation:

 

Begin the manuscript with a clear and concise introduction that outlines the importance of parent-child interaction, the challenges in manually coding DPICS, and the need for automated classification using AI.

Clearly state the research objectives and hypotheses that guide the study.

Methodology Section:

 

Provide a more detailed explanation of the DPICS coding system for readers who may not be familiar with it.

Explain the selection process of datasets, including criteria for inclusion and any potential biases.

Describe the pre-processing steps for the data, such as text cleaning, tokenization, and data augmentation, if applicable.

Elaborate on the fine-tuning process for the RoBERTa model, including hyperparameters used and any modifications made to the model architecture.

Experimental Setup:

 

Describe the train-test split methodology and any cross-validation techniques used.

Provide information on the size of the training and testing datasets, as well as any data augmentation techniques applied.

Mention the hardware and software infrastructure used for training and evaluation.

Results Section:

 

Present the results in a clear and organized manner using tables and figures.

Provide statistical significance tests, if applicable, to support the claim that the RoBERTa model outperforms other methods.

Include a discussion of the potential reasons for the observed results, such as the complexity of DPICS codes or the characteristics of the datasets.

Discussion and Implications:

 

Discuss the practical implications of the study's findings. How can automated DPICS code classification benefit parent-child interaction therapists and researchers?

Highlight the limitations of the study, such as dataset size, potential biases, and any challenges encountered during the fine-tuning process.

Offer suggestions for future research in this area, such as exploring the use of larger datasets or different AI models.

Conclusion:

 

Summarize the main findings of the study and their significance.

Restate how the use of the RoBERTa model can assist experts in the labeling process.

End with a strong concluding statement that reinforces the importance of the research.

References:

 

Ensure that all references are properly cited and listed in a consistent citation style (e.g., APA, MLA).

Clarity and Language:

 

Review the manuscript for clarity and coherence, ensuring that the flow of ideas is logical and easy to follow.

Carefully proofread the document for grammar, punctuation, and spelling errors.

Abstract:

 

Write a clear and concise abstract that summarizes the key points of the study, including the problem addressed, methodology, results, and implications.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

In this article, the authors focus on the quality of parent-child interaction (PCI). As the authors correctly argue that PCI significantly impact the child's cognitive and socio-emotional development.

 

The authors claim that RoBERTa outperformed other text mining/NLP methods when applied to the proposed datasets.

This is an interesting manuscript. However, there are some issues that should be addressed.

 

1. Some more details on the approach used should be added to the methodology. For example, could you include more details on the architecture?

2. The evaluation section should be expanded to include more details on the dataset, and the evaluation process?

Good quality. Minor typos

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

I am happy with the revised manuscript and I recommend its publication