@inproceedings{biesialska-etal-2019-talp,
title = "The {TALP}-{UPC} System for the {WMT} Similar Language Task: Statistical vs Neural Machine Translation",
author = "Biesialska, Magdalena and
Guardia, Lluis and
Costa-juss{\`a}, Marta R.",
editor = "Bojar, Ond{\v{r}}ej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5424",
doi = "10.18653/v1/W19-5424",
pages = "185--191",
abstract = "Although the problem of similar language translation has been an area of research interest for many years, yet it is still far from being solved. In this paper, we study the performance of two popular approaches: statistical and neural. We conclude that both methods yield similar results; however, the performance varies depending on the language pair. While the statistical approach outperforms the neural one by a difference of 6 BLEU points for the Spanish-Portuguese language pair, the proposed neural model surpasses the statistical one by a difference of 2 BLEU points for Czech-Polish. In the former case, the language similarity (based on perplexity) is much higher than in the latter case. Additionally, we report negative results for the system combination with back-translation. Our TALP-UPC system submission won 1st place for Czech-{\textgreater}Polish and 2nd place for Spanish-{\textgreater}Portuguese in the official evaluation of the 1st WMT Similar Language Translation task.",
}
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%0 Conference Proceedings
%T The TALP-UPC System for the WMT Similar Language Task: Statistical vs Neural Machine Translation
%A Biesialska, Magdalena
%A Guardia, Lluis
%A Costa-jussà, Marta R.
%Y Bojar, Ondřej
%Y Chatterjee, Rajen
%Y Federmann, Christian
%Y Fishel, Mark
%Y Graham, Yvette
%Y Haddow, Barry
%Y Huck, Matthias
%Y Yepes, Antonio Jimeno
%Y Koehn, Philipp
%Y Martins, André
%Y Monz, Christof
%Y Negri, Matteo
%Y Névéol, Aurélie
%Y Neves, Mariana
%Y Post, Matt
%Y Turchi, Marco
%Y Verspoor, Karin
%S Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F biesialska-etal-2019-talp
%X Although the problem of similar language translation has been an area of research interest for many years, yet it is still far from being solved. In this paper, we study the performance of two popular approaches: statistical and neural. We conclude that both methods yield similar results; however, the performance varies depending on the language pair. While the statistical approach outperforms the neural one by a difference of 6 BLEU points for the Spanish-Portuguese language pair, the proposed neural model surpasses the statistical one by a difference of 2 BLEU points for Czech-Polish. In the former case, the language similarity (based on perplexity) is much higher than in the latter case. Additionally, we report negative results for the system combination with back-translation. Our TALP-UPC system submission won 1st place for Czech-\textgreaterPolish and 2nd place for Spanish-\textgreaterPortuguese in the official evaluation of the 1st WMT Similar Language Translation task.
%R 10.18653/v1/W19-5424
%U https://aclanthology.org/W19-5424
%U https://doi.org/10.18653/v1/W19-5424
%P 185-191
Markdown (Informal)
[The TALP-UPC System for the WMT Similar Language Task: Statistical vs Neural Machine Translation](https://aclanthology.org/W19-5424) (Biesialska et al., WMT 2019)
ACL