Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components

Raul Vazquez; Michele Boggia; Alessandro Raganato; Niki A. Loppi; Stig-Arne Grönroos; Jörg Tiedemann

Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components

Raul Vazquez , Michele Boggia, Alessandro Raganato, Niki A. Loppi, Stig-Arne Grönroos, Jörg Tiedemann

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

Abstrakti

We give an update on the Found in Translation (FoTran) project, focusing on the study of emerging language-agnostic representations from neural machine translation (NMT). We describe our attention-bridge model, a modular NMT model which connects language-specific components through a shared network layer. Our latest implementation supports distributed training over many nodes and GPUs in order to substantially scale up the number of languages that can be included in a modern neural translation architecture.

Alkuperäiskieli	englanti
Otsikko	Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
Toimittajat	Helana Moniz, Lieve Macken, Andrew Rufener, et al.
Sivumäärä	2
Julkaisupaikka	Geneva
Kustantaja	European Association for Machine Translation
Julkaisupäivä	2022
Sivut	311-312
ISBN (elektroninen)	9789464597622
Tila	Julkaistu - 2022
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	Annual Conference of the European Association for Machine Translation - Ghent, Belgia Kesto: 1 kesäk. 2022 → 3 kesäk. 2022 Konferenssinumero: 23 https://eamt2022.com

Tieteenalat

6121 Kielitieteet
113 Tietojenkäsittely- ja informaatiotieteet

Pääsy asiakirjaan

2022.eamt-1.45Lopullinen julkaistu versio, 223 KBLisenssi: CC BY-NC-SA

https://aclanthology.org/2022.eamt-1.45Lisenssi: CC BY-NC-SA

FoTran: Found in Translation - Natural Language Understanding with Cross-Lingual Grounding
Tiedemann, J. (Projektinjohtaja), Attieh, J. (osallistuja), Aulamo, M. (osallistuja), Celikkanat, H. (osallistuja), De Gibert Bonet, O. (osallistuja), Grönroos, S.-A. (osallistuja), Mickus, T. (osallistuja), Raganato, A. (osallistuja), Scherrer, Y. (osallistuja), Silfverberg, M. (osallistuja), Sjöblom, E. I. (osallistuja), Talman, A. (osallistuja), Vazquez , R. (osallistuja), Virpioja, S. P. (osallistuja), Yli-Jyrä, A. (osallistuja), Zosa, E. (osallistuja), Celikkanat, H. (Osallistuja), Raganato, A. (Osallistuja), Silfverberg, M. (Osallistuja), Sulubacak, U. (Osallistuja) & Vazquez , R. (Osallistuja)
01/09/2018 → 31/03/2024
Projekti: EU Horizon 2020: European Research Council: Consolidator Grant (H2020-ERC-COG)

Siteeraa tätä

Vazquez , R., Boggia, M., Raganato, A., Loppi, N. A., Grönroos, S.-A., & Tiedemann, J. (2022). Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components. teoksessa H. Moniz, L. Macken, & A. Rufener, et al. (Toimittajat), Proceedings of the 23rd Annual Conference of the European Association for Machine Translation (Sivut 311-312). European Association for Machine Translation. https://aclanthology.org/2022.eamt-1.45

Vazquez , Raul ; Boggia, Michele ; Raganato, Alessandro et al. / Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components. Proceedings of the 23rd Annual Conference of the European Association for Machine Translation. Toimittaja / Helana Moniz ; Lieve Macken ; Andrew Rufener, et al. Geneva : European Association for Machine Translation, 2022. Sivut 311-312

@inproceedings{3edfb254460443608c323d5a03040262,

title = "Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components",

abstract = "We give an update on the Found in Translation (FoTran) project, focusing on the study of emerging language-agnostic representations from neural machine translation (NMT). We describe our attention-bridge model, a modular NMT model which connects language-specific components through a shared network layer. Our latest implementation supports distributed training over many nodes and GPUs in order to substantially scale up the number of languages that can be included in a modern neural translation architecture.",

keywords = "6121 Languages, 113 Computer and information sciences",

author = "Raul Vazquez and Michele Boggia and Alessandro Raganato and Loppi, {Niki A.} and Stig-Arne Gr{\"o}nroos and J{\"o}rg Tiedemann",

year = "2022",

language = "English",

pages = "311--312",

editor = "Helana Moniz and Lieve Macken and {Rufener, et al.}, Andrew",

booktitle = "Proceedings of the 23rd Annual Conference of the European Association for Machine Translation",

publisher = "European Association for Machine Translation",

address = "Switzerland",

note = "Annual Conference of the European Association for Machine Translation ; Conference date: 01-06-2022 Through 03-06-2022",

url = "https://eamt2022.com",

}

Vazquez , R, Boggia, M, Raganato, A, Loppi, NA, Grönroos, S-A & Tiedemann, J 2022, Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components. julkaisussa H Moniz, L Macken & A Rufener, et al. (toim), Proceedings of the 23rd Annual Conference of the European Association for Machine Translation. European Association for Machine Translation, Geneva, Sivut 311-312, Annual Conference of the European Association for Machine Translation, Ghent, Belgia, 01/06/2022. <https://aclanthology.org/2022.eamt-1.45>

Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components. / Vazquez , Raul; Boggia, Michele; Raganato, Alessandro et al.
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation. toim. / Helana Moniz; Lieve Macken; Andrew Rufener, et al. Geneva: European Association for Machine Translation, 2022. s. 311-312.

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

TY - GEN

T1 - Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components

AU - Vazquez , Raul

AU - Boggia, Michele

AU - Raganato, Alessandro

AU - Loppi, Niki A.

AU - Grönroos, Stig-Arne

AU - Tiedemann, Jörg

N1 - Conference code: 23

PY - 2022

Y1 - 2022

N2 - We give an update on the Found in Translation (FoTran) project, focusing on the study of emerging language-agnostic representations from neural machine translation (NMT). We describe our attention-bridge model, a modular NMT model which connects language-specific components through a shared network layer. Our latest implementation supports distributed training over many nodes and GPUs in order to substantially scale up the number of languages that can be included in a modern neural translation architecture.

AB - We give an update on the Found in Translation (FoTran) project, focusing on the study of emerging language-agnostic representations from neural machine translation (NMT). We describe our attention-bridge model, a modular NMT model which connects language-specific components through a shared network layer. Our latest implementation supports distributed training over many nodes and GPUs in order to substantially scale up the number of languages that can be included in a modern neural translation architecture.

KW - 6121 Languages

KW - 113 Computer and information sciences

M3 - Conference contribution

SP - 311

EP - 312

BT - Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

A2 - Moniz, Helana

A2 - Macken, Lieve

A2 - Rufener, et al., Andrew

PB - European Association for Machine Translation

CY - Geneva

T2 - Annual Conference of the European Association for Machine Translation

Y2 - 1 June 2022 through 3 June 2022

ER -

Vazquez R, Boggia M, Raganato A, Loppi NA, Grönroos SA , Tiedemann J. Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components. julkaisussa Moniz H, Macken L, Rufener, et al. A, toimittajat, Proceedings of the 23rd Annual Conference of the European Association for Machine Translation. Geneva: European Association for Machine Translation. 2022. s. 311-312

Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components

Abstrakti

Tieteenalat

Pääsy asiakirjaan

Projektit

FoTran: Found in Translation - Natural Language Understanding with Cross-Lingual Grounding

Siteeraa tätä