Papers by Yingjun Du
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. |