neural-machine-translation-by-jointly-learning-to-align-and-translate-2021-018

Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio

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neural-machine-translation-by-jointly-learning-to-align-and-translate-2021-018

Dzmitry Bahdanau, Kyunghyun Cho, Yoshua BengioUFTAN UNIVERSITYLinguistics2021

Abstract

This record links to a foundational neural machine translation paper that introduced an attention-based alignment approach. It is useful for testing humanities-adjacent computational linguistics records and external document workflows.

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Author: Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio
Supervisor: Prof. Grace E. Amadi
Faculty: Arts
Department: Linguistics
Document type: Masters Thesis
Year: 2021
Keywords: neural machine translation, attention mechanism, sequence modelling, computational linguistics, language technology
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License: all_rights_reserved

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Auto-generated from the record metadata: authors, journal title, DOI, volume, issue/serial, pages and publication date. Review before final academic use.

Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio (2026). neural-machine-translation-by-jointly-learning-to-align-and-translate-2021-018 [Masters Thesis]. UFTAN UNIVERSITY. https://inrepscholar.com/uftan-university/projects/957

Source fields used: Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio

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neural-machine-translation-by-jointly-learning-to-align-and-translate-2021-018

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