NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation

MIHAEL ARCAN; Nivranshu Pasricha; Peter Paul Buitelaar

doi:http://hdl.handle.net/10379/16525

NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation

MIHAEL ARCAN, Nivranshu Pasricha, Peter Paul Buitelaar

Data Science Institute

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

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.

Original language	English (Ireland)
Title of host publication	3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+ 2020)
Place of Publication	Dublin, Ireland (Virtual)
DOIs	https://doi.org/http://hdl.handle.net/10379/16525
Publication status	Published - 1 Dec 2020

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Pasricha N; Arcan M; Buitelaar P

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http://hdl.handle.net/10379/16525

Cite this

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title = "NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation",

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.",

author = "MIHAEL ARCAN and Nivranshu Pasricha and Buitelaar, {Peter Paul}",

year = "2020",

month = dec,

day = "1",

doi = "http://hdl.handle.net/10379/16525",

language = "English (Ireland)",

booktitle = "3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+ 2020)",

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NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation. / ARCAN, MIHAEL; Pasricha, Nivranshu; Buitelaar, Peter Paul.
3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+ 2020). Dublin, Ireland (Virtual), 2020.

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

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PY - 2020/12/1

Y1 - 2020/12/1

N2 - 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.

AB - 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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CY - Dublin, Ireland (Virtual)

ER -

NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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