62 citations · 285 across the 35 of their papers we have counts for
7 papers · 1 filter
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training
Kristjan Arumae, Qing Sun, Parminder Bhatia
Pre-training large language models has become a standard in the natural language processing community. Such models are pre-trained on generic data (e.g. BookCorpus and English Wiki…
Improve black-box sequential anomaly detector relevancy with limited user feedback
Luyang Kong, Lifan Chen, Ming Chen +2
Anomaly detectors are often designed to catch statistical anomalies. End-users typically do not have interest in all of the detected outliers, but only those relevant to their appl…
COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature
Colby Wise, Vassilis N. Ioannidis, Miguel Romero Calvo +6
The coronavirus disease (COVID-19) has claimed the lives of over 350,000 people and infected more than 6 million people worldwide. Several search engines have surfaced to provide r…
AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature
Parminder Bhatia, Lan Liu, Kristjan Arumae +12
Coronavirus disease (COVID-19) has been declared as a pandemic by WHO with thousands of cases being reported each day. Numerous scientific articles are being published on the disea…
Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network
Shaoqing Yuan, Parminder Bhatia, Busra Celikkaya +2
Recent advancements in medical entity linking have been applied in the area of scientific literature and social media data. However, with the adoption of telemedicine and conversat…
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
Miguel Ballesteros, Rishita Anubhai, Shuai Wang +6
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events wit…
CALM: Continuous Adaptive Learning for Language Modeling
Kristjan Arumae, Parminder Bhatia
Training large language representation models has become a standard in the natural language processing community. This allows for fine tuning on any number of specific tasks, howev…