Measuring Domain Portability and ErrorPropagation in Biomedical QA

TL;DR

This paper evaluates the domain portability of biomedical QA systems based on BERT and investigates how error propagation from retrieval affects overall QA performance, highlighting the importance of domain-specific fine-tuning and high-quality retrieval.

Contribution

It provides an empirical analysis of domain transferability in biomedical QA and quantifies the impact of retrieval errors on system performance.

Findings

Domain-specific fine-tuning improves biomedical QA accuracy.

Retrieval errors cause significant performance drops, over 10 points.

High-quality retrieval is crucial for effective biomedical QA.

Abstract

In this work we present Google's submission to the BioASQ 7 biomedical question answering (QA) task (specifically Task 7b, Phase B). The core of our systems are based on BERT QA models, specifically the model of \cite{alberti2019bert}. In this report, and via our submissions, we aimed to investigate two research questions. We start by studying how domain portable are QA systems that have been pre-trained and fine-tuned on general texts, e.g., Wikipedia. We measure this via two submissions. The first is a non-adapted model that uses a public pre-trained BERT model and is fine-tuned on the Natural Questions data set \cite{kwiatkowski2019natural}. The second system takes this non-adapted model and fine-tunes it with the BioASQ training data. Next, we study the impact of error propagation in end-to-end retrieval and QA systems. Again we test this via two submissions. The first uses human annotated relevant documents and snippets as input to the model and the second predicted documents and snippets. Our main findings are that domain specific fine-tuning can benefit Biomedical QA. However, the biggest quality bottleneck is at the retrieval stage, where we see large drops in metrics -- over 10pts absolute -- when using non gold inputs to the QA model.