@inproceedings{pasricha-etal-2020-nuig,
title = "{NUIG}-{DSI} at the {W}eb{NLG}+ challenge: Leveraging Transfer Learning for {RDF}-to-text generation",
author = "Pasricha, Nivranshu and
Arcan, Mihael and
Buitelaar, Paul",
editor = "Castro Ferreira, Thiago and
Gardent, Claire and
Ilinykh, Nikolai and
van der Lee, Chris and
Mille, Simon and
Moussallem, Diego and
Shimorina, Anastasia",
booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
month = "12",
year = "2020",
address = "Dublin, Ireland (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.webnlg-1.15",
pages = "137--143",
abstract = "This paper describes the system submitted by NUIG-DSI to the WebNLG+ challenge 2020 in the RDF-to-text generation task for the English language. For this challenge, we leverage transfer learning by adopting the T5 model architecture for our submission and fine-tune the model on the WebNLG+ corpus. Our submission ranks among the top five systems for most of the automatic evaluation metrics achieving a BLEU score of 51.74 over all categories with scores of 58.23 and 45.57 across seen and unseen categories respectively.",
}
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<abstract>This paper describes the system submitted by NUIG-DSI to the WebNLG+ challenge 2020 in the RDF-to-text generation task for the English language. For this challenge, we leverage transfer learning by adopting the T5 model architecture for our submission and fine-tune the model on the WebNLG+ corpus. Our submission ranks among the top five systems for most of the automatic evaluation metrics achieving a BLEU score of 51.74 over all categories with scores of 58.23 and 45.57 across seen and unseen categories respectively.</abstract>
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%0 Conference Proceedings
%T NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation
%A Pasricha, Nivranshu
%A Arcan, Mihael
%A Buitelaar, Paul
%Y Castro Ferreira, Thiago
%Y Gardent, Claire
%Y Ilinykh, Nikolai
%Y van der Lee, Chris
%Y Mille, Simon
%Y Moussallem, Diego
%Y Shimorina, Anastasia
%S Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland (Virtual)
%F pasricha-etal-2020-nuig
%X This paper describes the system submitted by NUIG-DSI to the WebNLG+ challenge 2020 in the RDF-to-text generation task for the English language. For this challenge, we leverage transfer learning by adopting the T5 model architecture for our submission and fine-tune the model on the WebNLG+ corpus. Our submission ranks among the top five systems for most of the automatic evaluation metrics achieving a BLEU score of 51.74 over all categories with scores of 58.23 and 45.57 across seen and unseen categories respectively.
%U https://aclanthology.org/2020.webnlg-1.15
%P 137-143
Markdown (Informal)
[NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation](https://aclanthology.org/2020.webnlg-1.15) (Pasricha et al., WebNLG 2020)
ACL