Automatic Question Answering (QA) has been successfully applied in various
domains such as search engines and chatbots. Biomedical QA (BQA), as an
emerging QA task, enables innovative applications to effectively perceive,
access and understand complex biomedical knowledge. There have been tremendous
developments of BQA in the past two decades, which we classify into 5
distinctive approaches: classic, information retrieval, machine reading
comprehension, knowledge base and question entailment approaches. In this
survey, we introduce available datasets and representative methods of each BQA
approach in detail. Despite the developments, BQA systems are still immature
and rarely used in real-life settings. We identify and characterize several key
challenges in BQA that might lead to this issue, and discuss some potential
future directions to explore.

自动问答系统在不同领域有成功应用，生物医学问答是一项新兴任务。本文对生物医学问答的五种不同方法、可用数据集和代表性方法进行了详细介绍，并确定了该领域尚未成熟、面临的困境以及未来探索方向。

生物医学问题回答：方法与挑战综述

Biomedical Question Answering: A Survey of Approaches and Challenges

Biomedical question answering (QA) is a challenging task due to the scarcity
of data and the requirement of domain expertise. Pre-trained language models
have been used to address these issues. Recently, learning relationships
between sentence pairs has been proved to improve performance in general QA. In
this paper, we focus on applying BioBERT to transfer the knowledge of natural
language inference (NLI) to biomedical QA. We observe that BioBERT trained on
the NLI dataset obtains better performance on Yes/No (+5.59%), Factoid
(+0.53%), List type (+13.58%) questions compared to performance obtained in a
previous challenge (BioASQ 7B Phase B). We present a sequential transfer
learning method that significantly performed well in the 8th BioASQ Challenge
(Phase B). In sequential transfer learning, the order in which tasks are
fine-tuned is important. We measure an unanswerable rate of the extractive QA
setting when the formats of factoid and list type questions are converted to
the format of the Stanford Question Answering Dataset (SQuAD).

本文提出了一种在生物医学问答任务中应用预训练语言模型和迁移学习的方法，并通过实验证明优化迁移学习任务的顺序可以显著提高其性能。

自然语言推理在生物医学问答中的可迁移性

Transferability of Natural Language Inference to Biomedical Question  Answering

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.

本论文针对 BioASQ 7 生物医学问答任务（7b 阶段 B）进行研究，探讨了于一般文本（如维基百科）上进行预训练和微调的 QA 系统的领域移植性，并分析了端到端检索和 QA 系统中的误差传播对结果的影响。结果表明，领域特定的微调可以提高生物医学 QA 的质量，但检索阶段的质量仍是质量瓶颈。