Papers by Nithin Holla
Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation (2021.acl-long)
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| Challenge: | Existing methods for word sense disambiguation (WSD) lack large annotated datasets with sufficient coverage of words . performance of such methods lags behind fully-supervised methods . a meta-learning model is proposed to solve this problem . |
| Approach: | They propose a model of semantic memory for supervised word sense disambiguation using meta-learning. |
| Outcome: | The proposed model improves performance in few-shot WSD and produces meaning prototypes that capture similar senses of distinct words. |
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation (2020.findings-emnlp)
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| Challenge: | Existing methods for word sense disambiguation (WSD) are limited and require large datasets annotated with word senses. |
| Approach: | They propose a meta-learning framework for few-shot word sense disambiguation where the goal is to learn to disambiguate unseen words from only a few labeled instances. |
| Outcome: | The proposed framework is based on a large training dataset and a small number of examples. |