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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

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arxiv 2102.01672 v3 pith:4ERRQCUZ submitted 2021-02-02 cs.CL cs.AIcs.LG

Sebastian Gehrmann , Tosin Adewumi , Karmanya Aggarwal , Pawan Sasanka Ammanamanchi , Aremu Anuoluwapo , Antoine Bosselut , Khyathi Raghavi Chandu , Miruna Clinciu
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Dipanjan Das Kaustubh D. Dhole Wanyu Du Esin Durmus Ondřej Dušek Chris 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 Rubungo Andre Niyongabo 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
This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords evaluationmetricsmodelsbenchmarkgenerationlanguagenaturalprogress
verification ladder T0 review T1 audit T2 compute T3 formal
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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 which we are organizing a shared task at our ACL 2021 Workshop and to which we invite the entire NLG community to participate.

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