mlsanityData to 7 Oct 2026

Data Augmentation for Biomedical Factoid Question Answering

arXiv 2204.04711 ↗First posted 10 Apr 2022cs.CL cs.AIcited by 5

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Abstract (arXiv)

We study the effect of seven data augmentation (da) methods in factoid question answering, focusing on the biomedical domain, where obtaining training instances is particularly difficult. We experiment with data from the BioASQ challenge, which we augment with training instances obtained from an artificial biomedical machine reading comprehension dataset, or via back-translation, information retrieval, word substitution based on word2vec embeddings, or masked language modeling, question generation, or extending the given passage with additional context. We show that da can lead to very significant performance gains, even when using large pre-trained Transformers, contributing to a broader discussion of if/when da benefits large pre-trained models. One of the simplest da methods, word2vec-based word substitution, performed best and is recommended. We release our artificial training instances and code.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 56 of its 94 references that are in the map. First posted on arXiv 10 Apr 2022.

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56 of 94 references.

References · 56

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Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 1.

5 papers.

  1. 2024Question answering systems for health professionals at the point of care -- a systematic reviewcited by 3
  2. 2023A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasetscited by 2
  3. 2025Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026)cited by 2
  4. 2024Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systemscited by 0
  5. 2026Task-Centric Acceleration of Small-Language Modelscited by 0