Enhancing performance of transformer-based models in natural language understanding through word importance embedding-Reference-Cited by-同舟云学术

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Enhancing performance of transformer-based models in natural language understanding through word importance embedding

Published:2024-11 Issue: Volume:304 Page:112404
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Hong Seung-Kyu^ORCID,Jang Jae-Seok^ORCID,Kwon Hyuk-Yoon^ORCID

Funder

Seoul National University of Science and Technology

National Research Foundation of Korea

Publisher

Elsevier BV

Reference46 articles.

1. Exploring the limits of language modeling;Jozefowicz,2016

2. Improved backing-off for m-gram language modeling;Kneser,1995

3. Efficient estimation of word representations in vector space;Mikolov,2013

4. Sequence to sequence learning with neural networks;Sutskever,2014

5. Neural machine translation by jointly learning to align and translate;Bahdanau,2016

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