An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives

This repository contains the resources associated with the EMNLP 2023 paper:

Cho et al. (2023). "An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives".

You can find the paper HERE.

Contents in this repository are:

• /annotated_papers - Annotated papers' PDF files.
  • /aaai
  • /acl
  • /acm
  • /ieee
  • /pubmed
• LICENSE
• README.md - This file.
• full_table.csv - Full table contains all selected papers in the final list with attributes.

We only share annotated experiment/model papers. Some papers are not included due to copyright restrictions.

Below are the descriptions of each attributes in the full_table.csv:

• Title: The title of the selected paper.
• Link: The url of the paper.
• File Name: the corresponding pdf filename of the paper in our repository.
• Public Access: Whether the paper is publicly avilable. x indicates false. If false, the paper is not uploaded in our repository.
• Affiliation: Affiliation of the authors of the paper. cs: Computer Science, med: Medicine, joint: has authors from both fields, dns: did not specify.
• Database: The source of the paper.
• Paper Type: The type of the paper. We here only show model or experiment papers.
• Language: Target language used in this paper.
• Mental Health Category: Target mental health category in this paper.
• Target Group: Target group of this paper. Could be patients, caregivers, or clinicians.
• Target Demographic: Target demographic of this paper.
• Chatbot Name: The name of the chatbot model used in this paper.
• Chatbot Type: Type of the mental health CA. Could be QA, open domain, or task oriented.
• Model Technique: Type of technique used to build the model. Could be rule-based, retrieval-based, or generative.
• Off the Shelf: Information about the usage of off-the-shelf models in the system. We limit Off-the-shelf models to pre-trained models or applications. Could be yes (directly used), used as a part (off-the-shelf model consists a part of the pipeline), or finetuned.
• Oursourced Model Name: The name of the off-the-shelf model, if any.
• Training Data: The name or source of the training data, if any.
• Interface: Type of input the model takes. Could be text, voice, visual, or button.
• Embodiment: Embodiment of the model. Could be physical or visual.
• Platform: The platform the model run on. Could be Web, Mobile, PC, or other devices.
• Public Access: If the availability of the model is disclosed in the paper. Could be fully open (parameter level) or API (able to use).
• Study Design: Type of user study performed in the paper. Could be RCT (Randomized Controlled Trial), user study (ask participants to use and evaluate), or comparative analysis (divide users with different conditions and compare the result).
• Recruitment: How participants are recruited.
• Sample Size: Size of the participants.
• Duration: Duration taken for the user study.
• Automatic Evaluation: List of automatic evaluations used in this paper.
• Human Evaluation: List of Human Evaluation used in this paper.
• Statistical Test: List of statistical tests used for measuring significance in this paper.
• Ethics: Whether the paper mentioned ethical consideration. Could be IRB (Institutional Review Boards), or yes (mentioned in the paper).
• Statistical Test: Name of statistical tests used in this paper.