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.- Anthology ID:
- 2023.bionlp-1.34
- Volume:
- The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Dina Demner-fushman, Sophia Ananiadou, Kevin Cohen
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 369–376
- Language:
- URL:
- https://aclanthology.org/2023.bionlp-1.34
- DOI:
- 10.18653/v1/2023.bionlp-1.34
- Cite (ACL):
- Masaki Asada and Makoto Miwa. 2023. BioNART: A Biomedical Non-AutoRegressive Transformer for Natural Language Generation. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 369–376, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- BioNART: A Biomedical Non-AutoRegressive Transformer for Natural Language Generation (Asada & Miwa, BioNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.bionlp-1.34.pdf