Fabrice Harel-Canada


2024

pdf bib
Human-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking
Hong Jin Kang | Fabrice Harel-Canada | Muhammad Ali Gulzar | Nanyun Peng | Miryung Kim
Findings of the Association for Computational Linguistics: NAACL 2024

2023

pdf bib
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
Northern European Journal of Language Technology, Volume 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 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.

2022

pdf bib
Sibylvariant Transformations for Robust Text Classification
Fabrice Harel-Canada | Muhammad Ali Gulzar | Nanyun Peng | Miryung Kim
Findings of the Association for Computational Linguistics: ACL 2022

The vast majority of text transformation techniques in NLP are inherently limited in their ability to expand input space coverage due to an implicit constraint to preserve the original class label. In this work, we propose the notion of sibylvariance (SIB) to describe the broader set of transforms that relax the label-preserving constraint, knowably vary the expected class, and lead to significantly more diverse input distributions. We offer a unified framework to organize all data transformations, including two types of SIB: (1) Transmutations convert one discrete kind into another, (2) Mixture Mutations blend two or more classes together. To explore the role of sibylvariance within NLP, we implemented 41 text transformations, including several novel techniques like Concept2Sentence and SentMix. Sibylvariance also enables a unique form of adaptive training that generates new input mixtures for the most confused class pairs, challenging the learner to differentiate with greater nuance. Our experiments on six benchmark datasets strongly support the efficacy of sibylvariance for generalization performance, defect detection, and adversarial robustness.

pdf bib
EnDex: Evaluation of Dialogue Engagingness at Scale
Guangxuan Xu | Ruibo Liu | Fabrice Harel-Canada | Nischal Reddy Chandra | Nanyun Peng
Findings of the Association for Computational Linguistics: EMNLP 2022

We propose EnDex, the first human-reaction based model to evaluate dialogue engagingness. EnDex is trained on 80k Reddit-based Engagement Dataset (RED) curated using a novel distant-supervision framework. Engagingness is a key measure that captures high-level quality of AI dialogue systems and closely reflects actual user experience. However, data shortage, plus the abstract and extensive definition of engagingness makes it challenging to develop an automatic metric. Our work departs from mainstream approaches that use synthetic negative examples to train binary classifiers, and instead, proposes a solution using distant-supervision from human-reaction feedback. To support the soundness of our EnDex metric, we offer a theoretical foundation for engagement, an extensive ablation study, and empirical evidence of high correlation on five engagingness related datasets. We will release code, off-the-shelf EnDex model, and a large-scale dataset upon paper publication to facilitate future research.
Search
Co-authors