NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Abstract
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its transformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robustness analysis results are available publicly on the NL-Augmenter repository (https://github.com/GEM-benchmark/NL-Augmenter).
Cite
@article{arxiv.2112.02721,
title = {NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation},
author = {Kaustubh D. Dhole and Varun Gangal and Sebastian Gehrmann and Aadesh Gupta and Zhenhao Li and Saad Mahamood and Abinaya Mahendiran and Simon Mille and Ashish Shrivastava and Samson Tan and Tongshuang Wu and Jascha Sohl-Dickstein and Jinho D. Choi and Eduard Hovy and Ondrej Dusek and Sebastian Ruder and Sajant Anand and Nagender Aneja and Rabin Banjade and Lisa Barthe and Hanna Behnke and Ian Berlot-Attwell and Connor Boyle and Caroline Brun and Marco Antonio Sobrevilla Cabezudo and Samuel Cahyawijaya and Emile Chapuis and Wanxiang Che and Mukund Choudhary and Christian Clauss and Pierre Colombo and Filip Cornell and Gautier Dagan and Mayukh Das and Tanay Dixit and Thomas Dopierre and Paul-Alexis Dray and Suchitra Dubey and Tatiana Ekeinhor and Marco Di Giovanni and Tanya Goyal and Rishabh Gupta and Rishabh Gupta and Louanes Hamla and Sang Han and Fabrice Harel-Canada and Antoine Honore and Ishan Jindal and Przemyslaw K. Joniak and Denis Kleyko and Venelin Kovatchev and Kalpesh Krishna and Ashutosh Kumar and Stefan Langer and Seungjae Ryan Lee and Corey James Levinson and Hualou Liang and Kaizhao Liang and Zhexiong Liu and Andrey Lukyanenko and Vukosi Marivate and Gerard de Melo and Simon Meoni and Maxime Meyer and Afnan Mir and Nafise Sadat Moosavi and Niklas Muennighoff and Timothy Sum Hon Mun and Kenton Murray and Marcin Namysl and Maria Obedkova and Priti Oli and Nivranshu Pasricha and Jan Pfister and Richard Plant and Vinay Prabhu and Vasile Pais and Libo Qin and Shahab Raji and Pawan Kumar Rajpoot and Vikas Raunak and Roy Rinberg and Nicolas Roberts and Juan Diego Rodriguez and Claude Roux and Vasconcellos P. H. S. and Ananya B. Sai and Robin M. Schmidt and Thomas Scialom and Tshephisho Sefara and Saqib N. Shamsi and Xudong Shen and Haoyue Shi and Yiwen Shi and Anna Shvets and Nick Siegel and Damien Sileo and Jamie Simon and Chandan Singh and Roman Sitelew and Priyank Soni and Taylor Sorensen and William Soto and Aman Srivastava and KV Aditya Srivatsa and Tony Sun and Mukund Varma T and A Tabassum and Fiona Anting Tan and Ryan Teehan and Mo Tiwari and Marie Tolkiehn and Athena Wang and Zijian Wang and Gloria Wang and Zijie J. Wang and Fuxuan Wei and Bryan Wilie and Genta Indra Winata and Xinyi Wu and Witold Wydmański and Tianbao Xie and Usama Yaseen and Michael A. Yee and Jing Zhang and Yue Zhang},
journal= {arXiv preprint arXiv:2112.02721},
year = {2022}
}
Comments
39 pages, repository at https://github.com/GEM-benchmark/NL-Augmenter