NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
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
Abstract
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.- Anthology ID:
- 2023.nejlt-1.5
- Volume:
- Northern European Journal of Language Technology, Volume 9
- Month:
- Year:
- 2023
- Address:
- Linköping, Sweden
- Editor:
- Leon Derczynski
- Venue:
- NEJLT
- SIG:
- Publisher:
- Linköping University Electronic Press
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2023.nejlt-1.5
- DOI:
- https://doi.org/10.3384/nejlt.2000-1533.2023.4725
- Bibkey:
- Cite (ACL):
- 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, et al.. 2023. NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation. In Northern European Journal of Language Technology, Volume 9, Linköping, Sweden. Linköping University Electronic Press.
- Cite (Informal):
- NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation (Dhole et al., NEJLT 2023)
- Copy Citation:
- PDF:
- https://aclanthology.org/2023.nejlt-1.5.pdf
Export citation
@inproceedings{dhole-etal-2023-nl,
title = "{NL}-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation",
author = "Dhole, Kaustubh and
Gangal, Varun and
Gehrmann, Sebastian and
Gupta, Aadesh and
Li, Zhenhao and
Mahamood, Saad and
Mahadiran, Abinaya and
Mille, Simon and
Shrivastava, Ashish and
Tan, Samson and
Wu, Tongshang and
Sohl-Dickstein, Jascha and
Choi, Jinho and
Hovy, Eduard and
Du{\v{s}}ek, Ond{\v{r}}ej and
Ruder, Sebastian and
Anand, Sajant and
Aneja, Nagender and
Banjade, Rabin and
Barthe, Lisa and
Behnke, Hanna and
Berlot-Attwell, Ian and
Boyle, Connor and
Brun, Caroline and
Cabezudo, Marco Antonio Sobrevilla and
Cahyawijaya, Samuel and
Chapuis, Emile and
Che, Wanxiang and
Choudhary, Mukund and
Clauss, Christian and
Colombo, Pierre and
Cornell, Filip and
Dagan, Gautier and
Das, Mayukh and
Dixit, Tanay and
Dopierre, Thomas and
Dray, Paul-Alexis and
Dubey, Suchitra and
Ekeinhor, Tatiana and
Giovanni, Marco Di and
Goyal, Tanya and
Gupta, Rishabh and
Hamla, Louanes and
Han, Sang and
Harel-Canada, Fabrice and
Honor{\'e}, Antoine and
Jindal, Ishan and
Joniak, Przemys{\l}aw and
Kleyko, Denis and
Kovatchev, Venelin and
Krishna, Kalpesh and
Kumar, Ashutosh and
Langer, Stefan and
Lee, Seungjae Ryan and
Levinson, Corey James and
Liang, Hualou and
Liang, Kaizhao and
Liu, Zhexiong and
Lukyanenko, Andrey and
Marivate, Vukosi and
de Melo, Gerard and
Meoni, Simon and
Meyer, Maxine and
Mir, Afnan and
Moosavi, Nafise Sadat and
Meunnighoff, Niklas and
Mun, Timothy Sum Hon and
Murray, Kenton and
Namysl, Marcin and
Obedkova, Maria and
Oli, Priti and
Pasricha, Nivranshu and
Pfister, Jan and
Plant, Richard and
Prabhu, Vinay and
Pais, Vasile and
Qin, Libo and
Raji, Shahab and
Rajpoot, Pawan Kumar and
Raunak, Vikas and
Rinberg, Roy and
Roberts, Nicholas and
Rodriguez, Juan Diego and
Roux, Claude and
Samus, Vasconcellos and
Sai, Ananya and
Schmidt, Robin and
Scialom, Thomas and
Sefara, Tshephisho and
Shamsi, Saqib and
Shen, Xudong and
Shi, Yiwen and
Shi, Haoyue and
Shvets, Anna and
Siegel, Nick and
Sileo, Damien and
Simon, Jamie and
Singh, Chandan and
Sitelew, Roman and
Soni, Priyank and
Sorensen, Taylor and
Soto, William and
Srivastava, Aman and
Srivatsa, Aditya and
Sun, Tony and
Varma, Mukund and
Tabassum, A and
Tan, Fiona and
Teehan, Ryan and
Tiwari, Mo and
Tolkiehn, Marie and
Wang, Athena and
Wang, Zijian and
Wang, Zijie and
Wang, Gloria and
Wei, Fuxuan and
Wilie, Bryan and
Winata, Genta Indra and
Wu, Xinyu and
Wydmanski, Witold and
Xie, Tianbao and
Yaseen, Usama and
Yee, Michael and
Zhang, Jing and
Zhang, Yue",
editor = "Derczynski, Leon",
booktitle = "Northern European Journal of Language Technology, Volume 9",
year = "2023",
address = {Link{\"o}ping, Sweden},
publisher = {Link{\"o}ping University Electronic Press},
url = "https://aclanthology.org/2023.nejlt-1.5",
doi = "https://doi.org/10.3384/nejlt.2000-1533.2023.4725",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings %T NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation %A Dhole, Kaustubh %A Gangal, Varun %A Gehrmann, Sebastian %A Gupta, Aadesh %A Li, Zhenhao %A Mahamood, Saad %A Mahadiran, Abinaya %A Mille, Simon %A Shrivastava, Ashish %A Tan, Samson %A Wu, Tongshang %A Sohl-Dickstein, Jascha %A Choi, Jinho %A Hovy, Eduard %A Dušek, Ondřej %A Ruder, Sebastian %A Anand, Sajant %A Aneja, Nagender %A Banjade, Rabin %A Barthe, Lisa %A Behnke, Hanna %A Berlot-Attwell, Ian %A Boyle, Connor %A Brun, Caroline %A Cabezudo, Marco Antonio Sobrevilla %A Cahyawijaya, Samuel %A Chapuis, Emile %A Che, Wanxiang %A Choudhary, Mukund %A Clauss, Christian %A Colombo, Pierre %A Cornell, Filip %A Dagan, Gautier %A Das, Mayukh %A Dixit, Tanay %A Dopierre, Thomas %A Dray, Paul-Alexis %A Dubey, Suchitra %A Ekeinhor, Tatiana %A Giovanni, Marco Di %A Goyal, Tanya %A Gupta, Rishabh %A Hamla, Louanes %A Han, Sang %A Harel-Canada, Fabrice %A Honoré, Antoine %A Jindal, Ishan %A Joniak, Przemysław %A Kleyko, Denis %A Kovatchev, Venelin %A Krishna, Kalpesh %A Kumar, Ashutosh %A Langer, Stefan %A Lee, Seungjae Ryan %A Levinson, Corey James %A Liang, Hualou %A Liang, Kaizhao %A Liu, Zhexiong %A Lukyanenko, Andrey %A Marivate, Vukosi %A de Melo, Gerard %A Meoni, Simon %A Meyer, Maxine %A Mir, Afnan %A Moosavi, Nafise Sadat %A Meunnighoff, Niklas %A Mun, Timothy Sum Hon %A Murray, Kenton %A Namysl, Marcin %A Obedkova, Maria %A Oli, Priti %A Pasricha, Nivranshu %A Pfister, Jan %A Plant, Richard %A Prabhu, Vinay %A Pais, Vasile %A Qin, Libo %A Raji, Shahab %A Rajpoot, Pawan Kumar %A Raunak, Vikas %A Rinberg, Roy %A Roberts, Nicholas %A Rodriguez, Juan Diego %A Roux, Claude %A Samus, Vasconcellos %A Sai, Ananya %A Schmidt, Robin %A Scialom, Thomas %A Sefara, Tshephisho %A Shamsi, Saqib %A Shen, Xudong %A Shi, Yiwen %A Shi, Haoyue %A Shvets, Anna %A Siegel, Nick %A Sileo, Damien %A Simon, Jamie %A Singh, Chandan %A Sitelew, Roman %A Soni, Priyank %A Sorensen, Taylor %A Soto, William %A Srivastava, Aman %A Srivatsa, Aditya %A Sun, Tony %A Varma, Mukund %A Tabassum, A. %A Tan, Fiona %A Teehan, Ryan %A Tiwari, Mo %A Tolkiehn, Marie %A Wang, Athena %A Wang, Zijian %A Wang, Zijie %A Wang, Gloria %A Wei, Fuxuan %A Wilie, Bryan %A Winata, Genta Indra %A Wu, Xinyu %A Wydmanski, Witold %A Xie, Tianbao %A Yaseen, Usama %A Yee, Michael %A Zhang, Jing %A Zhang, Yue %Y Derczynski, Leon %S Northern European Journal of Language Technology, Volume 9 %D 2023 %I Linköping University Electronic Press %C Linköping, Sweden %F dhole-etal-2023-nl %X 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. %R https://doi.org/10.3384/nejlt.2000-1533.2023.4725 %U https://aclanthology.org/2023.nejlt-1.5 %U https://doi.org/https://doi.org/10.3384/nejlt.2000-1533.2023.4725
Markdown (Informal)
[NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://aclanthology.org/2023.nejlt-1.5) (Dhole et al., NEJLT 2023)
- NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation (Dhole et al., NEJLT 2023)
ACL
- 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, et al.. 2023. NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation. In Northern European Journal of Language Technology, Volume 9, Linköping, Sweden. Linköping University Electronic Press.