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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
Editors	Helana Moniz, Lieve Macken, Andrew Rufener, et al.
Number of pages	2
Place of Publication	Geneva
Publisher	European Association for Machine Translation
Publication date	2022
Pages	311-312
ISBN (Electronic)	9789464597622
Publication status	Published - 2022
MoE publication type	A4 Article in conference proceedings
Event	Annual Conference of the European Association for Machine Translation - Ghent, Belgium Duration: 1 Jun 2022 → 3 Jun 2022 Conference number: 23 https://eamt2022.com

Fields of Science

6121 Languages
113 Computer and information sciences

Access to Document

2022.eamt-1.45Final published version, 223 KBLicence: CC BY-NC-SA

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

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

Cite this

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. In H. Moniz, L. Macken, & A. Rufener, et al. (Eds.), Proceedings of the 23rd Annual Conference of the European Association for Machine Translation (pp. 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. editor / Helana Moniz ; Lieve Macken ; Andrew Rufener, et al. Geneva : European Association for Machine Translation, 2022. pp. 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. in H Moniz, L Macken & A Rufener, et al. (eds), Proceedings of the 23rd Annual Conference of the European Association for Machine Translation. European Association for Machine Translation, Geneva, pp. 311-312, Annual Conference of the European Association for Machine Translation, Ghent, Belgium, 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. ed. / Helana Moniz; Lieve Macken; Andrew Rufener, et al. Geneva: European Association for Machine Translation, 2022. p. 311-312.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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. In Moniz H, Macken L, Rufener, et al. A, editors, Proceedings of the 23rd Annual Conference of the European Association for Machine Translation. Geneva: European Association for Machine Translation. 2022. p. 311-312

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

Abstract

Fields of Science

Access to Document

Projects

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

Cite this