@inproceedings{de-gibert-bonet-etal-2022-unsupervised,
title = "Unsupervised Machine Translation in Real-World Scenarios",
author = "de Gibert Bonet, Ona and
Goenaga, Iakes and
Armengol-Estap{\'e}, Jordi and
Perez-de-Vi{\~n}aspre, Olatz and
Parra Escart{\'\i}n, Carla and
Sanchez, Marina and
Pinnis, M{\=a}rcis and
Labaka, Gorka and
Melero, Maite",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.325",
pages = "3038--3047",
abstract = "In this work, we present the work that has been carried on in the MT4All CEF project and the resources that it has generated by leveraging recent research carried out in the field of unsupervised learning. In the course of the project 18 monolingual corpora for specific domains and languages have been collected, and 12 bilingual dictionaries and translation models have been generated. As part of the research, the unsupervised MT methodology based only on monolingual corpora (Artetxe et al., 2017) has been tested on a variety of languages and domains. Results show that in specialised domains, when there is enough monolingual in-domain data, unsupervised results are comparable to those of general domain supervised translation, and that, at any rate, unsupervised techniques can be used to boost results whenever very little data is available.",
}
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<abstract>In this work, we present the work that has been carried on in the MT4All CEF project and the resources that it has generated by leveraging recent research carried out in the field of unsupervised learning. In the course of the project 18 monolingual corpora for specific domains and languages have been collected, and 12 bilingual dictionaries and translation models have been generated. As part of the research, the unsupervised MT methodology based only on monolingual corpora (Artetxe et al., 2017) has been tested on a variety of languages and domains. Results show that in specialised domains, when there is enough monolingual in-domain data, unsupervised results are comparable to those of general domain supervised translation, and that, at any rate, unsupervised techniques can be used to boost results whenever very little data is available.</abstract>
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%0 Conference Proceedings
%T Unsupervised Machine Translation in Real-World Scenarios
%A de Gibert Bonet, Ona
%A Goenaga, Iakes
%A Armengol-Estapé, Jordi
%A Perez-de-Viñaspre, Olatz
%A Parra Escartín, Carla
%A Sanchez, Marina
%A Pinnis, Mārcis
%A Labaka, Gorka
%A Melero, Maite
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F de-gibert-bonet-etal-2022-unsupervised
%X In this work, we present the work that has been carried on in the MT4All CEF project and the resources that it has generated by leveraging recent research carried out in the field of unsupervised learning. In the course of the project 18 monolingual corpora for specific domains and languages have been collected, and 12 bilingual dictionaries and translation models have been generated. As part of the research, the unsupervised MT methodology based only on monolingual corpora (Artetxe et al., 2017) has been tested on a variety of languages and domains. Results show that in specialised domains, when there is enough monolingual in-domain data, unsupervised results are comparable to those of general domain supervised translation, and that, at any rate, unsupervised techniques can be used to boost results whenever very little data is available.
%U https://aclanthology.org/2022.lrec-1.325
%P 3038-3047
Markdown (Informal)
[Unsupervised Machine Translation in Real-World Scenarios](https://aclanthology.org/2022.lrec-1.325) (de Gibert Bonet et al., LREC 2022)
ACL
- Ona de Gibert Bonet, Iakes Goenaga, Jordi Armengol-Estapé, Olatz Perez-de-Viñaspre, Carla Parra Escartín, Marina Sanchez, Mārcis Pinnis, Gorka Labaka, and Maite Melero. 2022. Unsupervised Machine Translation in Real-World Scenarios. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3038–3047, Marseille, France. European Language Resources Association.