Research Area:  Machine Learning

Abstract:

Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et al., 2018) to address the lack of high-quality, large-scale labeled scientific data. SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks. We evaluate on a suite of tasks including sequence tagging, sentence classification and dependency parsing, with datasets from a variety of scientific domains. We demonstrate statistically significant improvements over BERT and achieve new state-of-the-art results on several of these tasks.

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Author(s) Name:  Iz Beltagy, Kyle Lo, Arman Cohan

Publisher name:  arXiv:1903.10676

DOI:  10.48550/arXiv.1903.10676

Volume Information:  

Paper Link:   https://arxiv.org/abs/1903.10676