255 citations · 1k across the 16 of their papers we have counts for
19 papers
Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision
Sam Preston, Mu Wei, Rajesh Rao +11
Objective: The majority of detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Manual curation is expensive and ti…
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…
Self-supervised self-supervision by combining deep learning and probabilistic logic
Hunter Lang, Hoifung Poon
Labeling training examples at scale is a perennial challenge in machine learning. Self-supervision methods compensate for the lack of direct supervision by leveraging prior knowled…