# End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training

arXiv: https://arxiv.org/abs/2012.01414 · HTML: https://mlsanity.com/paper/2012.01414/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
- [Biomedical Question Answering: A Survey of Approaches and Challenges](https://mlsanity.com/paper/2102.05281/) (2021)
- [Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval](https://mlsanity.com/paper/2104.08801/) (2021)
- [Open-Domain Question-Answering for COVID-19 and Other Emergent Domains](https://mlsanity.com/paper/2110.06962/) (2021)
- [Establishing Strong Baselines for TripClick Health Retrieval](https://mlsanity.com/paper/2201.00365/) (2022)
- [In Situ Answer Sentence Selection at Web-scale](https://mlsanity.com/paper/2201.05984/) (2022)
- [COVIDRead: A Large-scale Question Answering Dataset on COVID-19](https://mlsanity.com/paper/2110.09321/) (2021)
- [Task Transfer and Domain Adaptation for Zero-Shot Question Answering](https://mlsanity.com/paper/2206.06705/) (2022)

## Abstract (arXiv)

End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages. Recent work has successfully trained neural IR systems using only supervised question answering (QA) examples from open-domain datasets. However, despite impressive performance on Wikipedia, neural IR lags behind traditional term matching approaches such as BM25 in more specific and specialized target domains such as COVID-19. Furthermore, given little or no labeled data, effective adaptation of QA systems can also be challenging in such target domains. In this work, we explore the application of synthetically generated QA examples to improve performance on closed-domain retrieval and MRC. We combine our neural IR and MRC systems and show significant improvements in end-to-end QA on the CORD-19 collection over a state-of-the-art open-domain QA baseline.