@inproceedings{brooks-youssef-2021-got,
title = "{GOT}: Testing for Originality in Natural Language Generation",
author = "Brooks, Jennifer and
Youssef, Abdou",
editor = "Bosselut, Antoine and
Durmus, Esin and
Gangal, Varun Prashant and
Gehrmann, Sebastian and
Jernite, Yacine and
Perez-Beltrachini, Laura and
Shaikh, Samira and
Xu, Wei",
booktitle = "Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.gem-1.7",
doi = "10.18653/v1/2021.gem-1.7",
pages = "68--72",
abstract = "We propose an approach to automatically test for originality in generation tasks where no standard automatic measures exist. Our proposal addresses original uses of language, not necessarily original ideas. We provide an algorithm for our approach and a run-time analysis. The algorithm, which finds all of the original fragments in a ground-truth corpus and can reveal whether a generated fragment copies an original without attribution, has a run-time complexity of theta(nlogn) where n is the number of sentences in the ground truth.",
}
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<abstract>We propose an approach to automatically test for originality in generation tasks where no standard automatic measures exist. Our proposal addresses original uses of language, not necessarily original ideas. We provide an algorithm for our approach and a run-time analysis. The algorithm, which finds all of the original fragments in a ground-truth corpus and can reveal whether a generated fragment copies an original without attribution, has a run-time complexity of theta(nlogn) where n is the number of sentences in the ground truth.</abstract>
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%0 Conference Proceedings
%T GOT: Testing for Originality in Natural Language Generation
%A Brooks, Jennifer
%A Youssef, Abdou
%Y Bosselut, Antoine
%Y Durmus, Esin
%Y Gangal, Varun Prashant
%Y Gehrmann, Sebastian
%Y Jernite, Yacine
%Y Perez-Beltrachini, Laura
%Y Shaikh, Samira
%Y Xu, Wei
%S Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F brooks-youssef-2021-got
%X We propose an approach to automatically test for originality in generation tasks where no standard automatic measures exist. Our proposal addresses original uses of language, not necessarily original ideas. We provide an algorithm for our approach and a run-time analysis. The algorithm, which finds all of the original fragments in a ground-truth corpus and can reveal whether a generated fragment copies an original without attribution, has a run-time complexity of theta(nlogn) where n is the number of sentences in the ground truth.
%R 10.18653/v1/2021.gem-1.7
%U https://aclanthology.org/2021.gem-1.7
%U https://doi.org/10.18653/v1/2021.gem-1.7
%P 68-72
Markdown (Informal)
[GOT: Testing for Originality in Natural Language Generation](https://aclanthology.org/2021.gem-1.7) (Brooks & Youssef, GEM 2021)
ACL