@inproceedings{de-mattei-etal-2021-human,
title = "Human Perception in Natural Language Generation",
author = "De Mattei, Lorenzo and
Lai, Huiyuan and
Dell{'}Orletta, Felice and
Nissim, Malvina",
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-1.2",
doi = "10.18653/v1/2021.gem-1.2",
pages = "15--23",
abstract = "We ask subjects whether they perceive as human-produced a bunch of texts, some of which are actually human-written, while others are automatically generated. We use this data to fine-tune a GPT-2 model to push it to generate more human-like texts, and observe that this fine-tuned model produces texts that are indeed perceived more human-like than the original model. Contextually, we show that our automatic evaluation strategy well correlates with human judgements. We also run a linguistic analysis to unveil the characteristics of human- vs machine-perceived language.",
}
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%0 Conference Proceedings
%T Human Perception in Natural Language Generation
%A De Mattei, Lorenzo
%A Lai, Huiyuan
%A Dell’Orletta, Felice
%A Nissim, Malvina
%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 de-mattei-etal-2021-human
%X We ask subjects whether they perceive as human-produced a bunch of texts, some of which are actually human-written, while others are automatically generated. We use this data to fine-tune a GPT-2 model to push it to generate more human-like texts, and observe that this fine-tuned model produces texts that are indeed perceived more human-like than the original model. Contextually, we show that our automatic evaluation strategy well correlates with human judgements. We also run a linguistic analysis to unveil the characteristics of human- vs machine-perceived language.
%R 10.18653/v1/2021.gem-1.2
%U https://aclanthology.org/2021.gem-1.2
%U https://doi.org/10.18653/v1/2021.gem-1.2
%P 15-23
Markdown (Informal)
[Human Perception in Natural Language Generation](https://aclanthology.org/2021.gem-1.2) (De Mattei et al., GEM 2021)
ACL
- Lorenzo De Mattei, Huiyuan Lai, Felice Dell’Orletta, and Malvina Nissim. 2021. Human Perception in Natural Language Generation. In Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), pages 15–23, Online. Association for Computational Linguistics.