# Building Extractive Question Answering System to Support Human-AI Health Coaching Model for Sleep Domain

arXiv: https://arxiv.org/abs/2305.19707 · HTML: https://mlsanity.com/paper/2305.19707/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 5 of 5)

- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [How Language Model Hallucinations Can Snowball](https://mlsanity.com/paper/2305.13534/) (2023)
- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets](https://mlsanity.com/paper/2304.00483/) (2023)

## Built on by (most cited first, 2 of 2)

- [Hierarchical Evaluation Framework: Best Practices for Human Evaluation](https://mlsanity.com/paper/2310.01917/) (2023)
- [SleepCoT: A Lightweight Personalized Sleep Health Model via Chain-of-Thought Distillation](https://mlsanity.com/paper/2410.16924/) (2024)

## Abstract (arXiv)

Non-communicable diseases (NCDs) are a leading cause of global deaths, necessitating a focus on primary prevention and lifestyle behavior change. Health coaching, coupled with Question Answering (QA) systems, has the potential to transform preventive healthcare. This paper presents a human-Artificial Intelligence (AI) health coaching model incorporating a domain-specific extractive QA system. A sleep-focused dataset, SleepQA, was manually assembled and used to fine-tune domain-specific BERT models. The QA system was evaluated using automatic and human methods. A data-centric framework enhanced the system's performance by improving passage retrieval and question reformulation. Although the system did not outperform the baseline in automatic evaluation, it excelled in the human evaluation of real-world questions. Integration into a Human-AI health coaching model was tested in a pilot Randomized Controlled Trial (RCT).