37 citations · 37 across the 5 of their papers we have counts for
7 papers · 1 filter
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP
Trapit Bansal, Karthick Gunasekaran, Tong Wang +2
Meta-learning considers the problem of learning an efficient learning process that can leverage its past experience to accurately solve new tasks. However, the efficacy of meta-lea…
Unsupervised Pre-training for Biomedical Question Answering
Vaishnavi Kommaraju, Karthick Gunasekaran, Kun Li +4
We explore the suitability of unsupervised representation learning methods on biomedical text -- BioBERT, SciBERT, and BioSentVec -- for biomedical question answering. To further i…
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks
Trapit Bansal, Rishikesh Jha, Tsendsuren Munkhdalai +1
Self-supervised pre-training of transformer models has revolutionized NLP applications. Such pre-training with language modeling objectives provides a useful initial point for para…
Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision
Trapit Bansal, Pat Verga, Neha Choudhary +1
Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonic…
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
Trapit Bansal, Rishikesh Jha, Andrew McCallum
Self-supervised pre-training of transformer models has shown enormous success in improving performance on a number of downstream tasks. However, fine-tuning on a new task still req…
Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling
Dung Thai, Shikhar Murty, Trapit Bansal +3
In textual information extraction and other sequence labeling tasks it is now common to use recurrent neural networks (such as LSTM) to form rich embedded representations of long-t…