@inproceedings{zaninello-magnini-2023-smashed,
title = "A smashed glass cannot be full: Generation of Commonsense Explanations through Prompt-based Few-shot Learning",
author = "Zaninello, Andrea and
Magnini, Bernardo",
editor = "Dalvi Mishra, Bhavana and
Durrett, Greg and
Jansen, Peter and
Neves Ribeiro, Danilo and
Wei, Jason",
booktitle = "Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)",
month = jun,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.nlrse-1.3",
doi = "10.18653/v1/2023.nlrse-1.3",
pages = "18--29",
abstract = "We assume that providing explanations is a process to elicit implicit knowledge in human communication, and propose a general methodology to generate commonsense explanations from pairs of semantically related sentences. We take advantage of both prompting applied to large, encoder-decoder pre-trained language models, and few-shot learning techniques, such as pattern-exploiting training. Experiments run on the e-SNLI dataset show that the proposed method achieves state-of-the-art results on the explanation generation task, with a substantial reduction of labelled data. The obtained results open new perspective on a number of tasks involving the elicitation of implicit knowledge.",
}
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<abstract>We assume that providing explanations is a process to elicit implicit knowledge in human communication, and propose a general methodology to generate commonsense explanations from pairs of semantically related sentences. We take advantage of both prompting applied to large, encoder-decoder pre-trained language models, and few-shot learning techniques, such as pattern-exploiting training. Experiments run on the e-SNLI dataset show that the proposed method achieves state-of-the-art results on the explanation generation task, with a substantial reduction of labelled data. The obtained results open new perspective on a number of tasks involving the elicitation of implicit knowledge.</abstract>
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%0 Conference Proceedings
%T A smashed glass cannot be full: Generation of Commonsense Explanations through Prompt-based Few-shot Learning
%A Zaninello, Andrea
%A Magnini, Bernardo
%Y Dalvi Mishra, Bhavana
%Y Durrett, Greg
%Y Jansen, Peter
%Y Neves Ribeiro, Danilo
%Y Wei, Jason
%S Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)
%D 2023
%8 June
%I Association for Computational Linguistics
%C Toronto, Canada
%F zaninello-magnini-2023-smashed
%X We assume that providing explanations is a process to elicit implicit knowledge in human communication, and propose a general methodology to generate commonsense explanations from pairs of semantically related sentences. We take advantage of both prompting applied to large, encoder-decoder pre-trained language models, and few-shot learning techniques, such as pattern-exploiting training. Experiments run on the e-SNLI dataset show that the proposed method achieves state-of-the-art results on the explanation generation task, with a substantial reduction of labelled data. The obtained results open new perspective on a number of tasks involving the elicitation of implicit knowledge.
%R 10.18653/v1/2023.nlrse-1.3
%U https://aclanthology.org/2023.nlrse-1.3
%U https://doi.org/10.18653/v1/2023.nlrse-1.3
%P 18-29
Markdown (Informal)
[A smashed glass cannot be full: Generation of Commonsense Explanations through Prompt-based Few-shot Learning](https://aclanthology.org/2023.nlrse-1.3) (Zaninello & Magnini, NLRSE 2023)
ACL