NL-Augmenter 🦎 → 🐍 A Framework for Task-Sensitive Natural Language Augmentation
Authors: Dhole Kaustubh , Gangal Varun , Gehrmann Sebastian , Gupta Aadesh , Li Zhenhao , Mahamood Saad , Mahadiran Abinaya , Mille Simon , Shrivastava Ashish , Tan Samson , Tongshang Wu  , Sohl-Dickstein Jascha , Choi Jinho , Hovy Eduard , Dušek Ondřej , Ruder Sebastian , Anand Sajant , Aneja Nagender , Banjade Rabin , Barthe Lisa , Behnke Hanna , Berlot-Attwell Ian , Boyle Connor , Brun Caroline , Sobrevilla Cabezudo Marco Antonio , Cahyawijaya Samuel , Chapuis Emile , Che Wanxiang , Choudhary Mukund , Clauss Christian , Colombo Pierre , Cornell Filip , Dagan Gautier , Das Mayukh , Dixit Tanay , Dopierre Thomas , Dray Paul-Alexis , Dubey Suchitra , Ekeinhor Tatiana , Di Giovanni Marco , Goyal Tanya , Gupta Rishabh , Hamla Louanes , Han Sang , Harel-Canada Fabrice , Honoré Antoine , Jindal Ishan , Joniak Przemysław , Kleyko Denis , Kovatchev Venelin , Krishna Kalpesh , Kumar Ashutosh , Langer Stefan , Lee Seungjae Ryan , Levinson Corey James , Liang Hualou , Liang Kaizhao , Liu Zhexiong , Lukyanenko Andrey , Marivate Vukosi , De Melo Gerard , Meoni Simon , Meyer Maxine , Mir Afnan , Moosavi Nafise Sadat , Meunnighoff Niklas , Mun Timothy Sum Hon , Murray Kenton , Namysl Marcin , Obedkova Maria , Oli Priti , Pasricha Nivranshu , Pfister Jan , Plant Richard , Prabhu Vinay , Pais Vasile , Qin Libo , Raji Shahab , Rajpoot Pawan Kumar , Raunak Vikas , Rinberg Roy , Roberts Nicholas , Rodriguez Juan Diego , Roux Claude , Samus Vasconcellos , Sai Ananya , Schmidt Robin , Scialom Thomas , Sefara Tshephisho , Shamsi Saqib , Shen Xudong , Shi Yiwen , Shi Haoyue , Shvets Anna , Siegel Nick , Sileo Damien , Simon Jamie , Singh Chandan , Sitelew Roman , Soni Priyank , Sorensen Taylor , Soto William , Srivastava Aman , Srivatsa Aditya , Sun Tony , Varma Mukund , Tabassum A , Tan Fiona , Teehan Ryan , Tiwari Mo , Tolkiehn Marie , Wang Athena , Wang Zijian , Wang Zijie , Wang Gloria , Wei Fuxuan , Wilie Bryan , Winata Genta Indra , Wu Xinyu , Wydmanski Witold , Xie Tianbao , Yaseen Usama , Yee Michael , Zhang Jing , Zhang Yue 
Published: April 2023

Data augmentation is an important method for evaluating the robustness of and enhancing the diversity of training data for natural language processing (NLP) models. In this paper, we present NL-Augmenter, a new participatory Python-based natural language (NL) augmentation framework which supports the creation of 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 NL tasks annotated with noisy descriptive tags. The transformations incorporate noise, intentional and accidental human mistakes, socio-linguistic variation, semantically-valid style, syntax changes, as well as artificial constructs that are unambiguous to humans. We demonstrate the efficacy of NL-Augmenter by using its transformations to analyze the robustness of popular language models. We find different models to be differently challenged on different tasks, with quasi-systematic score decreases. The infrastructure, datacards, and robustness evaluation results are publicly available on GitHub for the benefit of researchers working on paraphrase generation, robustness analysis, and low-resource NLP.
El aumento de datos es un método importante para evaluar la solidez y mejorar la diversidad del entrenamiento datos para modelos de procesamiento de lenguaje natural (NLP). इस लेख में, हम एनएल-ऑगमेंटर का प्रस्ताव करते हैं - एक नया भागी- दारी पूर्वक, पायथन में बनाया गया, लैंग्वेज (एनएल) ऑग्मेंटेशन फ्रेमवर्क जो ट्रांसफॉर्मेशन (डेटा में बदलाव करना) और फीलटर (फीचर्स के अनुसार डेटा का भाग करना) के नीरमान का समर्थन करता है।. 我们描述了NL-Augmenter框架及其初步包含的117种转换和23个过滤器，并 大致标注分类了一系列可适配的自然语言任务. این دگرگونی ها شامل نویز، اشتباهات عمدی و تصادفی انسانی، تنوع اجتماعی-زبانی، سبک معنایی معتبر، تغییرات نحوی و همچنین ساختارهای مصنوعی است که برای انسان ها مبهم است. NL-Augmenterpa allin kaynintam qawachiyku, tikrakuyninku- nata servichikuspayku, chaywanmi qawariyku modelos de lenguaje popular nisqapa allin takyasqa kayninta. Kami menemukan model yang berbeda ditantang secara berbeda pada tugas yang berbeda, dengan penurunan skor kuasi-sistematis. Infrastruktur, kartu data, dan hasil evaluasi ketahanan dipublikasikan tersedia secara gratis di GitHub untuk kepentingan para peneliti yang mengerjakan pembuatan parafrase, analisis ketahanan, dan NLP sumber daya rendah.