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SciFive: a text-to-text transformer model for biomedical literature

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arxiv 2106.03598 v1 pith:Q5OMFTH3 submitted 2021-05-28 cs.CL cs.AIcs.LG

Long N. Phan , James T. Anibal , Hieu Tran , Shaurya Chanana , Erol Bahadroglu , Alec Peltekian , Grégoire Altan-Bonnet This is my paper
classification cs.CLcs.AIcs.LG
keywords biomedicalmethodsmodeltasksrelationscifiveareaarray
verification ladder T0 review T1 audit T2 compute T3 formal
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In this report, we introduce SciFive, a domain-specific T5 model that has been pre-trained on large biomedical corpora. Our model outperforms the current SOTA methods (i.e. BERT, BioBERT, Base T5) on tasks in named entity relation, relation extraction, natural language inference, and question-answering. We show that text-generation methods have significant potential in a broad array of biomedical NLP tasks, particularly those requiring longer, more complex outputs. Our results support the exploration of more difficult text generation tasks and the development of new methods in this area

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Cited by 2 Pith papers

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