P
Pawan Sasanka Ammanamanchi
Publications - 4
Citations - 76
Pawan Sasanka Ammanamanchi is an academic researcher. The author has contributed to research in topics: Natural language generation & Benchmark (computing). The author has an hindex of 2, co-authored 4 publications receiving 51 citations.
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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics.
Sebastian Gehrmann,Tosin P. Adewumi,Karmanya Aggarwal,Pawan Sasanka Ammanamanchi,Aremu Anuoluwapo,Antoine Bosselut,Khyathi Raghavi Chandu,Miruna 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,Rubungo Andre Niyongabo,Salomey Osei,Ankur P. 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 +55 more
TL;DR: GEM as discussed by the authors is a living benchmark for natural language generation (NLG), its Evaluation and Metrics, which provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested.
Proceedings ArticleDOI
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann,Tosin P. 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 P. 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 +55 more
TL;DR: GEM as discussed by the authors is a living benchmark for natural language generation (NLG), its Evaluation and Metrics, which provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested.
Proceedings ArticleDOI
Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards
Angelina McMillan-Major,Salomey Osei,Juan Diego Rodriguez,Pawan Sasanka Ammanamanchi,Sebastian Gehrmann,Yacine Jernite +5 more
TL;DR: In this paper, the authors present two case studies of efforts that aim to develop reusable documentation templates for datasets and models, including the HuggingFace data card, a general purpose card for datasets in NLP, and the GEM benchmark data and model cards with a focus on natural language generation.
Proceedings ArticleDOI
Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards
Angelina McMillan-Major,Salomey Osei,Juan Diego Rodriguez,Pawan Sasanka Ammanamanchi,Sebastian Gehrmann,Yacine Jernite +5 more
TL;DR: In this paper, the authors present two case studies of efforts that aim to develop reusable documentation templates for datasets and models, including the HuggingFace data card, a general purpose card for datasets in NLP, and the GEM benchmark data and model cards with a focus on natural language generation.