@inproceedings{martinez-murillo-etal-2025-key,
title = "Where and How as Key Factors for Knowledge-Enhanced Constrained Commonsense Generation",
author = "Martinez-Murillo, Ivan and
Moreda Pozo, Paloma and
Lloret, Elena",
editor = "Angelova, Galia and
Kunilovskaya, Maria and
Escribe, Marie and
Mitkov, Ruslan",
booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
month = sep,
year = "2025",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2025.ranlp-1.80/",
pages = "694--703",
abstract = "This paper addresses a key limitation in Natural Language Generation (NLG) systems: their struggle with commonsense reasoning, which is essential for generating contextually appropriate and plausible text. The study proposes an approach to enhance the commonsense reasoning abilities of NLG systems by integrating external knowledge framed in a constrained commonsense generation task. The paper investigates strategies for extracting and injecting external knowledge into pre-trained models, specifically BART and T5, in both base and large configurations. Experimental results show that incorporating external knowledge extracted with a simple strategy leads to significant improvements in performance, with the models achieving 88{\%} accuracy in generating plausible and correct sentences. When refined methods for knowledge extraction are applied, the accuracy further increases to 92{\%}. These findings underscore the crucial role of high-quality external knowledge in enhancing the commonsense reasoning capabilities of NLG systems, suggesting that such integration is vital for advancing their performance in real-world applications."
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<abstract>This paper addresses a key limitation in Natural Language Generation (NLG) systems: their struggle with commonsense reasoning, which is essential for generating contextually appropriate and plausible text. The study proposes an approach to enhance the commonsense reasoning abilities of NLG systems by integrating external knowledge framed in a constrained commonsense generation task. The paper investigates strategies for extracting and injecting external knowledge into pre-trained models, specifically BART and T5, in both base and large configurations. Experimental results show that incorporating external knowledge extracted with a simple strategy leads to significant improvements in performance, with the models achieving 88% accuracy in generating plausible and correct sentences. When refined methods for knowledge extraction are applied, the accuracy further increases to 92%. These findings underscore the crucial role of high-quality external knowledge in enhancing the commonsense reasoning capabilities of NLG systems, suggesting that such integration is vital for advancing their performance in real-world applications.</abstract>
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%0 Conference Proceedings
%T Where and How as Key Factors for Knowledge-Enhanced Constrained Commonsense Generation
%A Martinez-Murillo, Ivan
%A Moreda Pozo, Paloma
%A Lloret, Elena
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F martinez-murillo-etal-2025-key
%X This paper addresses a key limitation in Natural Language Generation (NLG) systems: their struggle with commonsense reasoning, which is essential for generating contextually appropriate and plausible text. The study proposes an approach to enhance the commonsense reasoning abilities of NLG systems by integrating external knowledge framed in a constrained commonsense generation task. The paper investigates strategies for extracting and injecting external knowledge into pre-trained models, specifically BART and T5, in both base and large configurations. Experimental results show that incorporating external knowledge extracted with a simple strategy leads to significant improvements in performance, with the models achieving 88% accuracy in generating plausible and correct sentences. When refined methods for knowledge extraction are applied, the accuracy further increases to 92%. These findings underscore the crucial role of high-quality external knowledge in enhancing the commonsense reasoning capabilities of NLG systems, suggesting that such integration is vital for advancing their performance in real-world applications.
%U https://aclanthology.org/2025.ranlp-1.80/
%P 694-703
Markdown (Informal)
[Where and How as Key Factors for Knowledge-Enhanced Constrained Commonsense Generation](https://aclanthology.org/2025.ranlp-1.80/) (Martinez-Murillo et al., RANLP 2025)
ACL