The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Anuoluwapo Aremu, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna-Adriana Clinciu, Dipanjan Das, Kaustubh Dhole, Wanyu Du, Esin Durmus, Ondřej Dušek, Chris Chinenye Emezue, Varun Gangal, Cristina Garbacea, Tatsunori Hashimoto, Yufang Hou, Yacine Jernite, Harsh Jhamtani, Yangfeng Ji, Shailza Jolly, Mihir Kale, Dhruv Kumar, Faisal Ladhak, Aman Madaan, Mounica Maddela, Khyati Mahajan, Saad Mahamood, Bodhisattwa Prasad Majumder, Pedro Henrique Martins, Angelina McMillan-Major, Simon Mille, Emiel van Miltenburg, Moin Nadeem, Shashi Narayan, Vitaly Nikolaev, Andre Niyongabo Rubungo, Salomey Osei, Ankur Parikh, Laura Perez-Beltrachini, Niranjan Ramesh Rao, Vikas Raunak, Juan Diego Rodriguez, Sashank Santhanam, João Sedoc, Thibault Sellam, Samira Shaikh, Anastasia Shimorina, Marco Antonio Sobrevilla Cabezudo, Hendrik Strobelt, Nishant Subramani, Wei Xu, Diyi Yang, Akhila Yerukola, Jiawei Zhou
Abstract
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.- Anthology ID:
- 2021.gem-1.10
- Volume:
- Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Antoine Bosselut, Esin Durmus, Varun Prashant Gangal, Sebastian Gehrmann, Yacine Jernite, Laura Perez-Beltrachini, Samira Shaikh, Wei Xu
- Venue:
- GEM
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 96–120
- Language:
- URL:
- https://aclanthology.org/2021.gem-1.10
- DOI:
- 10.18653/v1/2021.gem-1.10
- Bibkey:
- Cite (ACL):
- Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Anuoluwapo Aremu, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna-Adriana Clinciu, Dipanjan Das, Kaustubh Dhole, Wanyu Du, Esin Durmus, Ondřej Dušek, Chris Chinenye Emezue, Varun Gangal, Cristina Garbacea, Tatsunori Hashimoto, Yufang Hou, Yacine Jernite, et al.. 2021. The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics. In Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), pages 96–120, Online. Association for Computational Linguistics.
- Cite (Informal):
- The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (Gehrmann et al., GEM 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.gem-1.10.pdf
- Data
- GEM, ASSET, CommonGen, Czech restaurant information, DART, E2E, GLUE, MLSUM, SGD, ToTTo, TurkCorpus, WebNLG, WikiLingua, XSum
Export citation
@inproceedings{gehrmann-etal-2021-gem,
title = "The {GEM} Benchmark: Natural Language Generation, its Evaluation and Metrics",
author = "Gehrmann, Sebastian and
Adewumi, Tosin and
Aggarwal, Karmanya and
Ammanamanchi, Pawan Sasanka and
Aremu, Anuoluwapo and
Bosselut, Antoine and
Chandu, Khyathi Raghavi and
Clinciu, Miruna-Adriana and
Das, Dipanjan and
Dhole, Kaustubh and
Du, Wanyu and
Durmus, Esin and
Du{\v{s}}ek, Ond{\v{r}}ej and
Emezue, Chris Chinenye and
Gangal, Varun and
Garbacea, Cristina and
Hashimoto, Tatsunori and
Hou, Yufang and
Jernite, Yacine and
Jhamtani, Harsh and
Ji, Yangfeng and
Jolly, Shailza and
Kale, Mihir and
Kumar, Dhruv and
Ladhak, Faisal and
Madaan, Aman and
Maddela, Mounica and
Mahajan, Khyati and
Mahamood, Saad and
Majumder, Bodhisattwa Prasad and
Martins, Pedro Henrique and
McMillan-Major, Angelina and
Mille, Simon and
van Miltenburg, Emiel and
Nadeem, Moin and
Narayan, Shashi and
Nikolaev, Vitaly and
Niyongabo Rubungo, Andre and
Osei, Salomey and
Parikh, Ankur and
Perez-Beltrachini, Laura and
Rao, Niranjan Ramesh and
Raunak, Vikas and
Rodriguez, Juan Diego and
Santhanam, Sashank and
Sedoc, Jo{\~a}o and
Sellam, Thibault and
Shaikh, Samira and
Shimorina, Anastasia and
Sobrevilla Cabezudo, Marco Antonio and
Strobelt, Hendrik and
Subramani, Nishant and
Xu, Wei and
Yang, Diyi and
Yerukola, Akhila and
Zhou, Jiawei",
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-1.10",
doi = "10.18653/v1/2021.gem-1.10",
pages = "96--120",
abstract = "We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.",
}
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%0 Conference Proceedings %T The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics %A Gehrmann, Sebastian %A Adewumi, Tosin %A Aggarwal, Karmanya %A Ammanamanchi, Pawan Sasanka %A Aremu, Anuoluwapo %A Bosselut, Antoine %A Chandu, Khyathi Raghavi %A Clinciu, Miruna-Adriana %A Das, Dipanjan %A Dhole, Kaustubh %A Du, Wanyu %A Durmus, Esin %A Dušek, Ondřej %A Emezue, Chris Chinenye %A Gangal, Varun %A Garbacea, Cristina %A Hashimoto, Tatsunori %A Hou, Yufang %A Jernite, Yacine %A Jhamtani, Harsh %A Ji, Yangfeng %A Jolly, Shailza %A Kale, Mihir %A Kumar, Dhruv %A Ladhak, Faisal %A Madaan, Aman %A Maddela, Mounica %A Mahajan, Khyati %A Mahamood, Saad %A Majumder, Bodhisattwa Prasad %A Martins, Pedro Henrique %A McMillan-Major, Angelina %A Mille, Simon %A van Miltenburg, Emiel %A Nadeem, Moin %A Narayan, Shashi %A Nikolaev, Vitaly %A Niyongabo Rubungo, Andre %A Osei, Salomey %A Parikh, Ankur %A Perez-Beltrachini, Laura %A Rao, Niranjan Ramesh %A Raunak, Vikas %A Rodriguez, Juan Diego %A Santhanam, Sashank %A Sedoc, João %A Sellam, Thibault %A Shaikh, Samira %A Shimorina, Anastasia %A Sobrevilla Cabezudo, Marco Antonio %A Strobelt, Hendrik %A Subramani, Nishant %A Xu, Wei %A Yang, Diyi %A Yerukola, Akhila %A Zhou, Jiawei %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 gehrmann-etal-2021-gem %X We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop. %R 10.18653/v1/2021.gem-1.10 %U https://aclanthology.org/2021.gem-1.10 %U https://doi.org/10.18653/v1/2021.gem-1.10 %P 96-120
Markdown (Informal)
[The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://aclanthology.org/2021.gem-1.10) (Gehrmann et al., GEM 2021)
- The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (Gehrmann et al., GEM 2021)
ACL
- Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Anuoluwapo Aremu, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna-Adriana Clinciu, Dipanjan Das, Kaustubh Dhole, Wanyu Du, Esin Durmus, Ondřej Dušek, Chris Chinenye Emezue, Varun Gangal, Cristina Garbacea, Tatsunori Hashimoto, Yufang Hou, Yacine Jernite, et al.. 2021. The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics. In Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), pages 96–120, Online. Association for Computational Linguistics.