2 citations · 2 across the 4 of their papers we have counts for
8 papers
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…
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…
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…
Towards Annotating and Creating Sub-Sentence Summary Highlights
Kristjan Arumae, Parminder Bhatia, Fei Liu
Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences…
Guiding Extractive Summarization with Question-Answering Rewards
Kristjan Arumae, Fei Liu
Highlighting while reading is a natural behavior for people to track salient content of a document. It would be desirable to teach an extractive summarizer to do the same. However,…
Dynamic Transfer Learning for Named Entity Recognition
Parminder Bhatia, Kristjan Arumae, Busra Celikkaya
State-of-the-art named entity recognition (NER) systems have been improving continuously using neural architectures over the past several years. However, many tasks including NER r…