Bollegala, Danushka 
ORCID: 0000-0003-4476-7003 and O' Neill, James
(2022)
A Survey on Word Meta-Embedding Learning.
In: Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, 2022-7-23 - 2022-7-29, Vienna, Austria.
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Abstract
<jats:p>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.</jats:p>
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | Basic Behavioral and Social Science, Behavioral and Social Science |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 04 May 2022 13:49 |
| Last Modified: | 27 Apr 2024 20:16 |
| DOI: | 10.24963/ijcai.2022/758 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3154242 |
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