NL-Augmenter 🦎 → 🐍 A Framework for Task-Sensitive Natural Language Augmentation

Autor: Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahadiran, Simon Mille, Ashish Shrivastava, Samson Tan, null 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

Publikováno v: Northern European Journal of Language Technology. 9

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 lang

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e507b440433920e9d41b1179362c83ba
https://doi.org/10.3384/nejlt.2000-1533.2023.4725

Learning Disentangled Textual Representations via Statistical Measures of Similarity

Autor: Pierre Colombo, Guillaume Staerman, Nathan Noiry, Pablo Piantanida

When working with textual data, a natural application of disentangled representations is fair classification where the goal is to make predictions without being biased (or influenced) by sensitive attributes that may be present in the data (e.g., age

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a0e8760ae201d8b5547690b63349455
http://arxiv.org/abs/2205.03589

Code-switched inspired losses for spoken dialog representations

Autor: Pierre Colombo, Emile Chapuis, Matthieu Labeau, Chloé Clavel

Publikováno v: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::494a35326050abd79a7468166bf582ea
https://doi.org/10.18653/v1/2021.emnlp-main.656

The importance of fillers for text representations of speech transcripts

Autor: Matthieu Labeau, Pierre Colombo, Tanvi Dinkar, Chloé Clavel

Publikováno v: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Nov 2020, Online, Dominican Republic. pp.7985-7993, ⟨10.18653/v1/2020.emnlp-main.641⟩
EMNLP (1)

While being an essential component of spoken language, fillers (e.g."um" or "uh") often remain overlooked in Spoken Language Understanding (SLU) tasks. We explore the possibility of representing them with deep contextualised embeddings, showing impro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11f4a2b5a6b7ee34293e049558fb2561

Affect-Driven Dialog Generation

Autor: James Kennedy, Pierre Colombo, Wojciech Witon, Ashutosh Modi, Mubbasir Kapadia

Publikováno v: NAACL-HLT (1)
Scopus-Elsevier

The majority of current systems for end-to-end dialog generation focus on response quality without an explicit control over the affective content of the responses. In this paper, we present an affect-driven dialog system, which generates emotional re

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6be26cd826ecaa0cd09f269d5c368361
https://doi.org/10.18653/v1/n19-1374