@inproceedings{neale-etal-2016-word,
title = "Word Sense-Aware Machine Translation: Including Senses as Contextual Features for Improved Translation Models",
author = "Neale, Steven and
Gomes, Lu{\'\i}s and
Agirre, Eneko and
de Lacalle, Oier Lopez and
Branco, Ant{\'o}nio",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1441",
pages = "2777--2783",
abstract = "Although it is commonly assumed that word sense disambiguation (WSD) should help to improve lexical choice and improve the quality of machine translation systems, how to successfully integrate word senses into such systems remains an unanswered question. Some successful approaches have involved reformulating either WSD or the word senses it produces, but work on using traditional word senses to improve machine translation have met with limited success. In this paper, we build upon previous work that experimented on including word senses as contextual features in maxent-based translation models. Training on a large, open-domain corpus (Europarl), we demonstrate that this aproach yields significant improvements in machine translation from English to Portuguese.",
}
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%0 Conference Proceedings
%T Word Sense-Aware Machine Translation: Including Senses as Contextual Features for Improved Translation Models
%A Neale, Steven
%A Gomes, Luís
%A Agirre, Eneko
%A de Lacalle, Oier Lopez
%A Branco, António
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F neale-etal-2016-word
%X Although it is commonly assumed that word sense disambiguation (WSD) should help to improve lexical choice and improve the quality of machine translation systems, how to successfully integrate word senses into such systems remains an unanswered question. Some successful approaches have involved reformulating either WSD or the word senses it produces, but work on using traditional word senses to improve machine translation have met with limited success. In this paper, we build upon previous work that experimented on including word senses as contextual features in maxent-based translation models. Training on a large, open-domain corpus (Europarl), we demonstrate that this aproach yields significant improvements in machine translation from English to Portuguese.
%U https://aclanthology.org/L16-1441
%P 2777-2783
Markdown (Informal)
[Word Sense-Aware Machine Translation: Including Senses as Contextual Features for Improved Translation Models](https://aclanthology.org/L16-1441) (Neale et al., LREC 2016)
ACL