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

التفاصيل البيبلوغرافية
العنوان: The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
المؤلفون: Gehrmann, Sebastian, Adewumi, Tosin, Aggarwal, Karmanya, Ammanamanchi, Pawan Sasanka, Aremu, Anuoluwapo, Bosselut, Antoine, Chandu, Khyathi Raghavi, Clinciu, Miruna-Adriana, Das, Dipanjan, Dhole, Kaustubh, Du, Wanyu, Durmus, Esin, Dušek, Ondřej, Emezue, Chris Chinenye, Gangal, Varun, Garbacea, Cristina, Hashimoto, Tatsunori, Hou, Yufang, Jernite, Yacine, Jhamtani, Harsh, Ji, Yangfeng, Jolly, Shailza, Kale, Mihir, Kumar, Dhruv, Ladhak, Faisal, Madaan, Aman, Maddela, Mounica, Mahajan, Khyati, Mahamood, Saad, Majumder, Bodhisattwa Prasad, Martins, Pedro Henrique, Mcmillan-Major, Angelina, Mille, Simon, van Miltenburg, Emiel, Nadeem, Moin, Narayan, Shashi, Nikolaev, Vitaly, Niyongabo Rubungo, Andre, Osei, Salomey, Parikh, Ankur, Perez-Beltrachini, Laura, Rao, Niranjan Ramesh, Raunak, Vikas, Rodriguez, Juan Diego, Santhanam, Sashank, Sedoc, João, Sellam, Thibault, Shaikh, Samira, Shimorina, Anastasia, Sobrevilla Cabezudo, Marco Antonio, Strobelt, Hendrik, Subramani, Nishant, Xu, Wei, Yang, Diyi, Yerukola, Akhila, Zhou, Jiawei
المساهمون: Luleå University of Technology = Luleå Tekniska Universitet (LUT), Indraprastha Institute of Information Technology New Delhi (IIIT-Delhi), Natural Language Processing : representations, inference and semantics (SYNALP), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Harvard University
المصدر: Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) ; https://hal.science/hal-03466171Test ; Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), Aug 2021, Online, France. pp.96-120, ⟨10.18653/v1/2021.gem-1.10⟩
بيانات النشر: HAL CCSD
Association for Computational Linguistics
سنة النشر: 2021
المجموعة: Université de Lorraine: HAL
مصطلحات موضوعية: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
جغرافية الموضوع: Online, France
الوصف: International audience ; 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 ACL2021 Workshop and to which we invite the entire NLG community to participate.
نوع الوثيقة: conference object
اللغة: English
العلاقة: info:eu-repo/semantics/altIdentifier/arxiv/2102.01672; hal-03466171; https://hal.science/hal-03466171Test; ARXIV: 2102.01672
DOI: 10.18653/v1/2021.gem-1.10
الإتاحة: https://doi.org/10.18653/v1/2021.gem-1.10Test
https://hal.science/hal-03466171Test
رقم الانضمام: edsbas.DAA412E1
قاعدة البيانات: BASE