BioTABQA: Instruction Learning for Biomedical Table Question Answering

Man Luo, Sharad Saxena, Swaroop Mishra, Mihir Parmar, Chitta Baral

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)291-304
Number of pages14
JournalCEUR Workshop Proceedings
StatePublished - 2022
Event2022 Conference and Labs of the Evaluation Forum, CLEF 2022 - Bologna, Italy
Duration: Sep 5 2022Sep 8 2022


  • Table question answering
  • biomedical question answering
  • instruction learning
  • prompt learning

ASJC Scopus subject areas

  • Computer Science(all)


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