@inproceedings{asada-miwa-2023-bionart,
title = "{B}io{NART}: A Biomedical Non-{A}uto{R}egressive Transformer for Natural Language Generation",
author = "Asada, Masaki and
Miwa, Makoto",
editor = "Demner-fushman, Dina and
Ananiadou, Sophia and
Cohen, Kevin",
booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bionlp-1.34",
doi = "10.18653/v1/2023.bionlp-1.34",
pages = "369--376",
abstract = "We propose a novel Biomedical domain-specific Non-AutoRegressive Transformer model for natural language generation: BioNART. Our BioNART is based on an encoder-decoder model, and both encoder and decoder are compatible with widely used BERT architecture, which allows benefiting from publicly available pre-trained biomedical language model checkpoints. We performed additional pre-training and fine-tuned BioNART on biomedical summarization and doctor-patient dialogue tasks. Experimental results show that our BioNART achieves about 94{\%} of the ROUGE score to the pre-trained autoregressive model while realizing an 18 times faster inference speed on the iCliniq dataset.",
}
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%0 Conference Proceedings
%T BioNART: A Biomedical Non-AutoRegressive Transformer for Natural Language Generation
%A Asada, Masaki
%A Miwa, Makoto
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F asada-miwa-2023-bionart
%X We propose a novel Biomedical domain-specific Non-AutoRegressive Transformer model for natural language generation: BioNART. Our BioNART is based on an encoder-decoder model, and both encoder and decoder are compatible with widely used BERT architecture, which allows benefiting from publicly available pre-trained biomedical language model checkpoints. We performed additional pre-training and fine-tuned BioNART on biomedical summarization and doctor-patient dialogue tasks. Experimental results show that our BioNART achieves about 94% of the ROUGE score to the pre-trained autoregressive model while realizing an 18 times faster inference speed on the iCliniq dataset.
%R 10.18653/v1/2023.bionlp-1.34
%U https://aclanthology.org/2023.bionlp-1.34
%U https://doi.org/10.18653/v1/2023.bionlp-1.34
%P 369-376
Markdown (Informal)
[BioNART: A Biomedical Non-AutoRegressive Transformer for Natural Language Generation](https://aclanthology.org/2023.bionlp-1.34) (Asada & Miwa, BioNLP 2023)
ACL
