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    <abstract>In this paper, we present our approach to the GEM Shared Task at the INLG’24 Generation Challenges, which focuses on generating data-to-text in multiple languages, including low-resource languages, from WebNLG triples. We employ a combination of end-to-end and pipeline neural architectures for English text generation. To extend our methodology to Hindi, Korean, Arabic, and Swahili, we leverage a neural machine translation model. Our results demonstrate that our approach achieves competitive performance in the given task.</abstract>
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%0 Conference Proceedings
%T DCU-ADAPT-modPB at the GEM’24 Data-to-Text Generation Task: Model Hybridisation for Pipeline Data-to-Text Natural Language Generation
%A Osuji, Chinonso Cynthia
%A Huidrom, Rudali
%A Adebayo, Kolawole John
%A Castro Ferreira, Thiago
%A Davis, Brian
%Y Mille, Simon
%Y Clinciu, Miruna-Adriana
%S Proceedings of the 17th International Natural Language Generation Conference: Generation Challenges
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F osuji-etal-2024-dcu
%X In this paper, we present our approach to the GEM Shared Task at the INLG’24 Generation Challenges, which focuses on generating data-to-text in multiple languages, including low-resource languages, from WebNLG triples. We employ a combination of end-to-end and pipeline neural architectures for English text generation. To extend our methodology to Hindi, Korean, Arabic, and Swahili, we leverage a neural machine translation model. Our results demonstrate that our approach achieves competitive performance in the given task.
%U https://aclanthology.org/2024.inlg-genchal.7
%P 66-75

Markdown (Informal)

[DCU-ADAPT-modPB at the GEM’24 Data-to-Text Generation Task: Model Hybridisation for Pipeline Data-to-Text Natural Language Generation](https://aclanthology.org/2024.inlg-genchal.7) (Osuji et al., INLG 2024)

• DCU-ADAPT-modPB at the GEM’24 Data-to-Text Generation Task: Model Hybridisation for Pipeline Data-to-Text Natural Language Generation (Osuji et al., INLG 2024)

ACL

• Chinonso Cynthia Osuji, Rudali Huidrom, Kolawole John Adebayo, Thiago Castro Ferreira, and Brian Davis. 2024. DCU-ADAPT-modPB at the GEM’24 Data-to-Text Generation Task: Model Hybridisation for Pipeline Data-to-Text Natural Language Generation. In Proceedings of the 17th International Natural Language Generation Conference: Generation Challenges, pages 66–75, Tokyo, Japan. Association for Computational Linguistics.