Belz, Anya ORCID: 0000-0002-0552-8096, Shimorina, Anastasia, Popović, Maja ORCID: 0000-0001-8234-8745 and Reiter, Ehud ORCID: 0000-0002-7548-9504 (2022) The 2022 ReproGen shared task on reproducibility of evaluations in NLG: overview and results. In: 15th International Conference on Natural Language Generation: Generation Challenges, 17-22 July 2022, Waterville, ME, USA.
Abstract
Against a background of growing interest in reproducibility in NLP and ML, and as part of an ongoing research programme designed to develop theory and practice of reproducibility assessment in NLP, we organised the second shared task on reproducibility of evaluations in NLG, ReproGen 2022. This paper describes the shared task, summarises results from the reproduction studies submitted, and provides further comparative analysis of the results. Out of six initial team registrations, we received submissions from five teams. Meta-analysis of the five reproduction studies revealed varying degrees of reproducibility, and allowed further tentative conclusions about what types of eval- uation tend to have better reproducibility.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Conference |
Refereed: | Yes |
Subjects: | Computer Science > Computational linguistics Computer Science > Machine learning |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing Research Institutes and Centres > ADAPT |
Published in: | Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges. . Association for Computational Linguistics (ACL). |
Publisher: | Association for Computational Linguistics (ACL) |
Official URL: | https://aclanthology.org/2022.inlg-genchal.8 |
Copyright Information: | © 2022 Association for Computational Linguistics |
Funders: | ReproHum project on Investigating Reproducibility of Human Evaluations in Natural Language Processing, funded by EPSRC (UK) under grant number EP/V05645X/1., ADAPT ´ SFI Centre for Digital Media Technology which is funded by Science Foundation Ireland under Grant 13/RC/2106., European Regional Development Fund (ERDF) |
ID Code: | 28369 |
Deposited On: | 25 May 2023 14:50 by Maja Popovic . Last Modified 04 Jul 2023 10:39 |
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