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A Hierarchical Transformer for Unsupervised Parsing
In this paper, we extend the recent transformer model (Vaswani et al., 2017) by enabling it to learn hierarchical representations.
Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture
This paper presents our participation in the AGAC Track from the 2019 BioNLP Open Shared Tasks.
Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns
In this paper, we present a simple and effective method of extracting new facts with a pre-specified binary relationship type from the biomedical literature, without requiring any training data or hand-crafted rules.
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