Table Question Answering (TQA) is an important but under-explored task. Most
of the existing QA datasets are in unstructured text format and only few of
them use tables as the context. To the best of our knowledge, none of TQA
datasets exist in the biomedical domain where tables are frequently used to
present information. In this paper, we first curate a table question answering
dataset, BioTABQA, using 22 templates and the context from a biomedical
textbook on differential diagnosis. BioTABQA can not only be used to teach a
model how to answer questions from tables but also evaluate how a model
generalizes to unseen questions, an important scenario for biomedical
applications. To achieve the generalization evaluation, we divide the templates
into 17 training and 5 cross-task evaluations. Then, we develop two baselines
using single and multi-tasks learning on BioTABQA. Furthermore, we explore
instructional learning, a recent technique showing impressive generalizing
performance. Experimental results show that our instruction-tuned model
outperforms single and multi-task baselines on an average by ~23% and ~6%
across various evaluation settings, and more importantly, instruction-tuned
model outperforms baselines by ~5% on cross-tasks.

本文在生物医学领域维度构建了一个表格问答数据集 BioTABQA，包含 22 个模板和上下文中的信息。通过该数据集，本文提出了一种基于指导学习的方法，在多个评估方式下，该方法比单一和多任务基线模型表现提高约 23% 和 6%。最重要的是，在跨任务方面，该方法的性能比基线模型提高了约 5%。