A Survey onWord Meta-Embedding Learning

Bollegala, Danushka ORCID: 0000-0003-4476-7003 and O'Neill, James (2022) A Survey onWord Meta-Embedding Learning In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022-7-23 - 2022-7-29, Vienna, Austria.

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Abstract

Meta-embedding (ME) learning is an emerging approach that attempts to learn more accurate word embeddings given existing (source) word embeddings as the sole input. Due to their ability to incorporate semantics from multiple source embeddings in a compact manner with superior performance, ME learning has gained popularity among practitioners in NLP. To the best of our knowledge, there exist no prior systematic survey on ME learning and this paper attempts to fill this need. We classify ME learning methods according to multiple factors such as whether they (a) operate on static or contextualised embeddings, (b) trained in an unsupervised manner or (c) fine-tuned for a particular task/domain. Moreover, we discuss the limitations of existing ME learning methods and highlight potential future research directions.

Item Type:	Conference Item (Unspecified)
Uncontrolled Keywords:	46 Information and Computing Sciences, 4611 Machine Learning, Machine Learning and Artificial Intelligence
Divisions:	Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	04 May 2022 13:49
Last Modified:	23 May 2026 06:42
DOI:	10.24963/ijcai.2022/758
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3154242
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