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Found 10 papers, 2 papers with code
Kronecker Factorization for Preventing Catastrophic Forgetting in Large-scale Medical Entity Linking
Elastic Weight Consolidation is a recently proposed method to address this issue, but scaling this approach to the modern large models used in practice requires making strong independence assumptions about model parameters, limiting its effectiveness.
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training
However, in order to achieve state-of-the-art performance on out of domain tasks such as clinical named entity recognition and relation extraction, additional in domain pre-training is required.
AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature
Coronavirus disease (COVID-19) has been declared as a pandemic by WHO with thousands of cases being reported each day.
CALM: Continuous Adaptive Learning for Language Modeling
Training large language representation models has become a standard in the natural language processing community.
Towards Annotating and Creating Summary Highlights at Sub-sentence Level
Highlighting is a powerful tool to pick out important content and emphasize.
Towards Annotating and Creating Sub-Sentence Summary Highlights
Highlighting is a powerful tool to pick out important content and emphasize.
Guiding Extractive Summarization with Question-Answering Rewards
However, a major obstacle to the development of a supervised summarizer is the lack of ground-truth.
Dynamic Transfer Learning for Named Entity Recognition
We complement a standard hierarchical NER model with a general transfer learning framework consisting of parameter sharing between the source and target tasks, and showcase scores significantly above the baseline architecture.
A Study of Question Effectiveness Using Reddit "Ask Me Anything" Threads
Asking effective questions is a powerful social skill.
Reinforced Extractive Summarization with Question-Focused Rewards
We use reinforcement learning to explore the space of possible extractive summaries and introduce a question-focused reward function to promote concise, fluent, and informative summaries.
