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        <title>Data Augmentation for Biomedical Factoid Question Answering</title>
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        <namePart type="given">Dimitris</namePart>
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            <title>Proceedings of the 21st Workshop on Biomedical Language Processing</title>
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            <namePart type="given">Kevin</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.bionlp-1.6</identifier>
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%0 Conference Proceedings
%T Data Augmentation for Biomedical Factoid Question Answering
%A Pappas, Dimitris
%A Malakasiotis, Prodromos
%A Androutsopoulos, Ion
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 21st Workshop on Biomedical Language Processing
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F pappas-etal-2022-data
%X 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.
%R 10.18653/v1/2022.bionlp-1.6
%U https://aclanthology.org/2022.bionlp-1.6
%U https://doi.org/10.18653/v1/2022.bionlp-1.6
%P 63-81

Markdown (Informal)

[Data Augmentation for Biomedical Factoid Question Answering](https://aclanthology.org/2022.bionlp-1.6) (Pappas et al., BioNLP 2022)

• Data Augmentation for Biomedical Factoid Question Answering (Pappas et al., BioNLP 2022)

ACL

• Dimitris Pappas, Prodromos Malakasiotis, and Ion Androutsopoulos. 2022. Data Augmentation for Biomedical Factoid Question Answering. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 63–81, Dublin, Ireland. Association for Computational Linguistics.