Papers by Karthick Prasad Gunasekaran
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP (2021.emnlp-main)
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| Challenge: | Meta-learning considers learning as an efficient learning process that can leverage its past experience to accurately solve new tasks. |
| Approach: | They propose to provide task distributions for meta-learning by considering self-supervised tasks automatically proposed from unlabeled text to enable large-scale meta- learning in NLP. |
| Outcome: | The proposed distributions show that human learning models perform better on the few-shot benchmark than previous methods. |