NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
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 support...
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| Format: | Article |
| Language: | English |
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Linköping University Electronic Press
2023-04-01
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| Series: | Northern European Journal of Language Technology |
| Online Access: | https://nejlt.ep.liu.se/article/view/4725 |
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| author | Kaustubh Dhole Varun Gangal Sebastian Gehrmann Aadesh Gupta Zhenhao Li Saad Mahamood Abinaya Mahadiran Simon Mille Ashish Shrivastava Samson Tan Tongshang Wu Jascha Sohl-Dickstein Jinho Choi Eduard Hovy Ondřej Dušek Sebastian Ruder Sajant Anand Nagender Aneja Rabin Banjade Lisa Barthe Hanna Behnke Ian Berlot-Attwell Connor Boyle Caroline Brun Marco Antonio Sobrevilla Cabezudo Samuel Cahyawijaya Emile Chapuis Wanxiang Che Mukund Choudhary Christian Clauss Pierre Colombo Filip Cornell Gautier Dagan Mayukh Das Tanay Dixit Thomas Dopierre Paul-Alexis Dray Suchitra Dubey Tatiana Ekeinhor Marco Di Giovanni Tanya Goyal Rishabh Gupta Louanes Hamla Sang Han Fabrice Harel-Canada Antoine Honoré Ishan Jindal Przemysław Joniak Denis Kleyko Venelin Kovatchev Kalpesh Krishna Ashutosh Kumar Stefan Langer Seungjae Ryan Lee Corey James Levinson Hualou Liang Kaizhao Liang Zhexiong Liu Andrey Lukyanenko Vukosi Marivate Gerard de Melo Simon Meoni Maxine Meyer Afnan Mir Nafise Sadat Moosavi Niklas Meunnighoff Timothy Sum Hon Mun Kenton Murray Marcin Namysl Maria Obedkova Priti Oli Nivranshu Pasricha Jan Pfister Richard Plant Vinay Prabhu Vasile Pais Libo Qin Shahab Raji Pawan Kumar Rajpoot Vikas Raunak Roy Rinberg Nicholas Roberts Juan Diego Rodriguez Claude Roux Vasconcellos Samus Ananya Sai Robin Schmidt Thomas Scialom Tshephisho Sefara Saqib Shamsi Xudong Shen Yiwen Shi Haoyue Shi Anna Shvets Nick Siegel Damien Sileo Jamie Simon Chandan Singh Roman Sitelew Priyank Soni Taylor Sorensen William Soto Aman Srivastava Aditya Srivatsa Tony Sun Mukund Varma A Tabassum Fiona Tan Ryan Teehan Mo Tiwari Marie Tolkiehn Athena Wang Zijian Wang Zijie Wang Gloria Wang Fuxuan Wei Bryan Wilie Genta Indra Winata Xinyu Wu Witold Wydmanski Tianbao Xie Usama Yaseen Michael Yee Jing Zhang Yue Zhang |
| author_facet | Kaustubh Dhole Varun Gangal Sebastian Gehrmann Aadesh Gupta Zhenhao Li Saad Mahamood Abinaya Mahadiran Simon Mille Ashish Shrivastava Samson Tan Tongshang Wu Jascha Sohl-Dickstein Jinho Choi Eduard Hovy Ondřej Dušek Sebastian Ruder Sajant Anand Nagender Aneja Rabin Banjade Lisa Barthe Hanna Behnke Ian Berlot-Attwell Connor Boyle Caroline Brun Marco Antonio Sobrevilla Cabezudo Samuel Cahyawijaya Emile Chapuis Wanxiang Che Mukund Choudhary Christian Clauss Pierre Colombo Filip Cornell Gautier Dagan Mayukh Das Tanay Dixit Thomas Dopierre Paul-Alexis Dray Suchitra Dubey Tatiana Ekeinhor Marco Di Giovanni Tanya Goyal Rishabh Gupta Louanes Hamla Sang Han Fabrice Harel-Canada Antoine Honoré Ishan Jindal Przemysław Joniak Denis Kleyko Venelin Kovatchev Kalpesh Krishna Ashutosh Kumar Stefan Langer Seungjae Ryan Lee Corey James Levinson Hualou Liang Kaizhao Liang Zhexiong Liu Andrey Lukyanenko Vukosi Marivate Gerard de Melo Simon Meoni Maxine Meyer Afnan Mir Nafise Sadat Moosavi Niklas Meunnighoff Timothy Sum Hon Mun Kenton Murray Marcin Namysl Maria Obedkova Priti Oli Nivranshu Pasricha Jan Pfister Richard Plant Vinay Prabhu Vasile Pais Libo Qin Shahab Raji Pawan Kumar Rajpoot Vikas Raunak Roy Rinberg Nicholas Roberts Juan Diego Rodriguez Claude Roux Vasconcellos Samus Ananya Sai Robin Schmidt Thomas Scialom Tshephisho Sefara Saqib Shamsi Xudong Shen Yiwen Shi Haoyue Shi Anna Shvets Nick Siegel Damien Sileo Jamie Simon Chandan Singh Roman Sitelew Priyank Soni Taylor Sorensen William Soto Aman Srivastava Aditya Srivatsa Tony Sun Mukund Varma A Tabassum Fiona Tan Ryan Teehan Mo Tiwari Marie Tolkiehn Athena Wang Zijian Wang Zijie Wang Gloria Wang Fuxuan Wei Bryan Wilie Genta Indra Winata Xinyu Wu Witold Wydmanski Tianbao Xie Usama Yaseen Michael Yee Jing Zhang Yue Zhang |
| author_sort | Kaustubh Dhole |
| collection | DOAJ |
| description |
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.
|
| format | Article |
| id | doaj-art-c2e428cb80c34ffcab7e50faa55c61bd |
| institution | Kabale University |
| issn | 2000-1533 |
| language | English |
| publishDate | 2023-04-01 |
| publisher | Linköping University Electronic Press |
| record_format | Article |
| series | Northern European Journal of Language Technology |
| spelling | doaj-art-c2e428cb80c34ffcab7e50faa55c61bd2025-01-22T15:25:16ZengLinköping University Electronic PressNorthern European Journal of Language Technology2000-15332023-04-019110.3384/nejlt.2000-1533.2023.4725NL-Augmenter: A Framework for Task-Sensitive Natural Language AugmentationKaustubh Dhole0Varun GangalSebastian GehrmannAadesh GuptaZhenhao LiSaad MahamoodAbinaya MahadiranSimon MilleAshish ShrivastavaSamson TanTongshang WuJascha Sohl-DicksteinJinho ChoiEduard HovyOndřej DušekSebastian RuderSajant AnandNagender AnejaRabin BanjadeLisa BartheHanna BehnkeIan Berlot-AttwellConnor BoyleCaroline BrunMarco Antonio Sobrevilla CabezudoSamuel CahyawijayaEmile ChapuisWanxiang CheMukund ChoudharyChristian ClaussPierre ColomboFilip CornellGautier DaganMayukh DasTanay DixitThomas DopierrePaul-Alexis DraySuchitra DubeyTatiana EkeinhorMarco Di GiovanniTanya GoyalRishabh GuptaLouanes HamlaSang HanFabrice Harel-CanadaAntoine HonoréIshan JindalPrzemysław JoniakDenis KleykoVenelin KovatchevKalpesh KrishnaAshutosh KumarStefan LangerSeungjae Ryan LeeCorey James LevinsonHualou LiangKaizhao LiangZhexiong LiuAndrey LukyanenkoVukosi MarivateGerard de MeloSimon MeoniMaxine MeyerAfnan MirNafise Sadat MoosaviNiklas MeunnighoffTimothy Sum Hon MunKenton MurrayMarcin NamyslMaria ObedkovaPriti OliNivranshu PasrichaJan PfisterRichard PlantVinay PrabhuVasile PaisLibo QinShahab RajiPawan Kumar RajpootVikas RaunakRoy RinbergNicholas RobertsJuan Diego RodriguezClaude RouxVasconcellos SamusAnanya SaiRobin SchmidtThomas ScialomTshephisho SefaraSaqib ShamsiXudong ShenYiwen ShiHaoyue ShiAnna ShvetsNick SiegelDamien SileoJamie SimonChandan SinghRoman SitelewPriyank SoniTaylor SorensenWilliam SotoAman SrivastavaAditya SrivatsaTony SunMukund VarmaA TabassumFiona TanRyan TeehanMo TiwariMarie TolkiehnAthena WangZijian WangZijie WangGloria WangFuxuan WeiBryan WilieGenta Indra WinataXinyu WuWitold WydmanskiTianbao XieUsama YaseenMichael YeeJing ZhangYue ZhangEmory University 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. https://nejlt.ep.liu.se/article/view/4725 |
| spellingShingle | Kaustubh Dhole Varun Gangal Sebastian Gehrmann Aadesh Gupta Zhenhao Li Saad Mahamood Abinaya Mahadiran Simon Mille Ashish Shrivastava Samson Tan Tongshang Wu Jascha Sohl-Dickstein Jinho Choi Eduard Hovy Ondřej Dušek Sebastian Ruder Sajant Anand Nagender Aneja Rabin Banjade Lisa Barthe Hanna Behnke Ian Berlot-Attwell Connor Boyle Caroline Brun Marco Antonio Sobrevilla Cabezudo Samuel Cahyawijaya Emile Chapuis Wanxiang Che Mukund Choudhary Christian Clauss Pierre Colombo Filip Cornell Gautier Dagan Mayukh Das Tanay Dixit Thomas Dopierre Paul-Alexis Dray Suchitra Dubey Tatiana Ekeinhor Marco Di Giovanni Tanya Goyal Rishabh Gupta Louanes Hamla Sang Han Fabrice Harel-Canada Antoine Honoré Ishan Jindal Przemysław Joniak Denis Kleyko Venelin Kovatchev Kalpesh Krishna Ashutosh Kumar Stefan Langer Seungjae Ryan Lee Corey James Levinson Hualou Liang Kaizhao Liang Zhexiong Liu Andrey Lukyanenko Vukosi Marivate Gerard de Melo Simon Meoni Maxine Meyer Afnan Mir Nafise Sadat Moosavi Niklas Meunnighoff Timothy Sum Hon Mun Kenton Murray Marcin Namysl Maria Obedkova Priti Oli Nivranshu Pasricha Jan Pfister Richard Plant Vinay Prabhu Vasile Pais Libo Qin Shahab Raji Pawan Kumar Rajpoot Vikas Raunak Roy Rinberg Nicholas Roberts Juan Diego Rodriguez Claude Roux Vasconcellos Samus Ananya Sai Robin Schmidt Thomas Scialom Tshephisho Sefara Saqib Shamsi Xudong Shen Yiwen Shi Haoyue Shi Anna Shvets Nick Siegel Damien Sileo Jamie Simon Chandan Singh Roman Sitelew Priyank Soni Taylor Sorensen William Soto Aman Srivastava Aditya Srivatsa Tony Sun Mukund Varma A Tabassum Fiona Tan Ryan Teehan Mo Tiwari Marie Tolkiehn Athena Wang Zijian Wang Zijie Wang Gloria Wang Fuxuan Wei Bryan Wilie Genta Indra Winata Xinyu Wu Witold Wydmanski Tianbao Xie Usama Yaseen Michael Yee Jing Zhang Yue Zhang NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation Northern European Journal of Language Technology |
| title | NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation |
| title_full | NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation |
| title_fullStr | NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation |
| title_full_unstemmed | NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation |
| title_short | NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation |
| title_sort | nl augmenter a framework for task sensitive natural language augmentation |
| url | https://nejlt.ep.liu.se/article/view/4725 |
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