@inproceedings{chiruzzo-etal-2022-translating,
title = "Translating {S}panish into {S}panish {S}ign {L}anguage: Combining Rules and Data-driven Approaches",
author = "Chiruzzo, Luis and
McGill, Euan and
Egea-G{\'o}mez, Santiago and
Saggion, Horacio",
editor = "Ojha, Atul Kr. and
Liu, Chao-Hong and
Vylomova, Ekaterina and
Abbott, Jade and
Washington, Jonathan and
Oco, Nathaniel and
Pirinen, Tommi A and
Malykh, Valentin and
Logacheva, Varvara and
Zhao, Xiaobing",
booktitle = "Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.loresmt-1.10",
pages = "75--83",
abstract = "This paper presents a series of experiments on translating between spoken Spanish and Spanish Sign Language glosses (LSE), including enriching Neural Machine Translation (NMT) systems with linguistic features, and creating synthetic data to pretrain and later on finetune a neural translation model. We found evidence that pretraining over a large corpus of LSE synthetic data aligned to Spanish sentences could markedly improve the performance of the translation models.",
}
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%0 Conference Proceedings
%T Translating Spanish into Spanish Sign Language: Combining Rules and Data-driven Approaches
%A Chiruzzo, Luis
%A McGill, Euan
%A Egea-Gómez, Santiago
%A Saggion, Horacio
%Y Ojha, Atul Kr.
%Y Liu, Chao-Hong
%Y Vylomova, Ekaterina
%Y Abbott, Jade
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Pirinen, Tommi A.
%Y Malykh, Valentin
%Y Logacheva, Varvara
%Y Zhao, Xiaobing
%S Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F chiruzzo-etal-2022-translating
%X This paper presents a series of experiments on translating between spoken Spanish and Spanish Sign Language glosses (LSE), including enriching Neural Machine Translation (NMT) systems with linguistic features, and creating synthetic data to pretrain and later on finetune a neural translation model. We found evidence that pretraining over a large corpus of LSE synthetic data aligned to Spanish sentences could markedly improve the performance of the translation models.
%U https://aclanthology.org/2022.loresmt-1.10
%P 75-83
Markdown (Informal)
[Translating Spanish into Spanish Sign Language: Combining Rules and Data-driven Approaches](https://aclanthology.org/2022.loresmt-1.10) (Chiruzzo et al., LoResMT 2022)
ACL