Rethinking the Design of Sequence-to-Sequence Models for Efficient Machine Translation

Maha Elbayad


Anthology ID:
2022.eamt-1.1
Volume:
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
Month:
June
Year:
2022
Address:
Ghent, Belgium
Editors:
Helena Moniz, Lieve Macken, Andrew Rufener, Loïc Barrault, Marta R. Costa-jussà, Christophe Declercq, Maarit Koponen, Ellie Kemp, Spyridon Pilos, Mikel L. Forcada, Carolina Scarton, Joachim Van den Bogaert, Joke Daems, Arda Tezcan, Bram Vanroy, Margot Fonteyne
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation
Note:
Pages:
5–6
Language:
URL:
https://aclanthology.org/2022.eamt-1.1
DOI:
Bibkey:
Cite (ACL):
Maha Elbayad. 2022. Rethinking the Design of Sequence-to-Sequence Models for Efficient Machine Translation. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 5–6, Ghent, Belgium. European Association for Machine Translation.
Cite (Informal):
Rethinking the Design of Sequence-to-Sequence Models for Efficient Machine Translation (Elbayad, EAMT 2022)
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PDF:
https://preview.aclanthology.org/improve-issue-templates/2022.eamt-1.1.pdf