@inproceedings{aditya-hari-etal-2023-webnlg,
title = "{W}eb{NLG} Challenge 2023: Domain Adaptive Machine Translation for Low-Resource Multilingual {RDF}-to-Text Generation ({W}eb{NLG} 2023)",
author = "Aditya Hari, Kancharla and
Singh, Bhavyajeet and
Sharma, Anubhav and
Varma, Vasudeva",
editor = "Gatt, Albert and
Gardent, Claire and
Cripwell, Liam and
Belz, Anya and
Borg, Claudia and
Erdem, Aykut and
Erdem, Erkut",
booktitle = "Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)",
month = sep,
year = "2023",
address = "Prague, Czech Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.mmnlg-1.11",
pages = "93--94",
abstract = "This paper presents our submission to the WebNLG Challenge 2023 for generating text in several low-resource languages from RDF-triples. Our submission focuses on using machine translation for generating texts in Irish, Maltese, Welsh and Russian. While a simple and straightfoward approach, recent works have shown that using monolingual models for inference for multilingual tasks with the help of machine translation (translate-test) can out-perform multilingual models and training multilingual models on machine-translated data (translate-train) through careful tuning of the MT component. Our results show that this approach demonstrates competitive performance for this task even with limited data.",
}
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%0 Conference Proceedings
%T WebNLG Challenge 2023: Domain Adaptive Machine Translation for Low-Resource Multilingual RDF-to-Text Generation (WebNLG 2023)
%A Aditya Hari, Kancharla
%A Singh, Bhavyajeet
%A Sharma, Anubhav
%A Varma, Vasudeva
%Y Gatt, Albert
%Y Gardent, Claire
%Y Cripwell, Liam
%Y Belz, Anya
%Y Borg, Claudia
%Y Erdem, Aykut
%Y Erdem, Erkut
%S Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023)
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czech Republic
%F aditya-hari-etal-2023-webnlg
%X This paper presents our submission to the WebNLG Challenge 2023 for generating text in several low-resource languages from RDF-triples. Our submission focuses on using machine translation for generating texts in Irish, Maltese, Welsh and Russian. While a simple and straightfoward approach, recent works have shown that using monolingual models for inference for multilingual tasks with the help of machine translation (translate-test) can out-perform multilingual models and training multilingual models on machine-translated data (translate-train) through careful tuning of the MT component. Our results show that this approach demonstrates competitive performance for this task even with limited data.
%U https://aclanthology.org/2023.mmnlg-1.11
%P 93-94
Markdown (Informal)
[WebNLG Challenge 2023: Domain Adaptive Machine Translation for Low-Resource Multilingual RDF-to-Text Generation (WebNLG 2023)](https://aclanthology.org/2023.mmnlg-1.11) (Aditya Hari et al., MMNLG-WS 2023)
ACL