Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy

• URL: http://arxiv.org/abs/2110.02204v2
• Date: Wed, 6 Oct 2021 10:30:37 GMT
• Title: Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy
• Authors: Yi Zhou and Danushka Bollegala
• Abstract summary: We propose Context Derived Embeddings of Senses (CDES) CDES extracts sense related information from contextualised embeddings and injects it into static embeddings to create sense-specific static embeddings. We show that CDES can accurately learn sense-specific static embeddings reporting comparable performance to the current state-of-the-art sense embeddings.
• Score: 26.385418377513332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contextualised word embeddings generated from Neural Language Models (NLMs), such as BERT, represent a word with a vector that considers the semantics of the target word as well its context. On the other hand, static word embeddings such as GloVe represent words by relatively low-dimensional, memory- and compute-efficient vectors but are not sensitive to the different senses of the word. We propose Context Derived Embeddings of Senses (CDES), a method that extracts sense related information from contextualised embeddings and injects it into static embeddings to create sense-specific static embeddings. Experimental results on multiple benchmarks for word sense disambiguation and sense discrimination tasks show that CDES can accurately learn sense-specific static embeddings reporting comparable performance to the current state-of-the-art sense embeddings.

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