2.2k citations · 6.2k across the 59 of their papers we have counts for
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Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing
Robert Tinn, Hao Cheng, Yu Gu +5
Motivation: A perennial challenge for biomedical researchers and clinical practitioners is to stay abreast with the rapid growth of publications and medical notes. Natural language…
Knowledge-Rich Self-Supervision for Biomedical Entity Linking
Sheng Zhang, Hao Cheng, Shikhar Vashishth +6
Entity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification ap…
Modular Self-Supervision for Document-Level Relation Extraction
Sheng Zhang, Cliff Wong, Naoto Usuyama +3
Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining…
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning
Hoifung Poon, Hai Wang, Hunter Lang
Deep learning has proven effective for various application tasks, but its applicability is limited by the reliance on annotated examples. Self-supervised learning has emerged as a…
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
Yu Wang, Jinchao Li, Tristan Naumann +12
Information overload is a prevalent challenge in many high-value domains. A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hund…
Targeted Adversarial Training for Natural Language Understanding
Lis Pereira, Xiaodong Liu, Hao Cheng +3
We present a simple yet effective Targeted Adversarial Training (TAT) algorithm to improve adversarial training for natural language understanding. The key idea is to introspect cu…