Publications

Technological Solutions to Online Toxicity: Potential and Pitfalls

Arezo Bodaghi

Ketra A. Schmitt

Social media platforms present a perplexing duality, acting at once as sites to build community and a sense of belonging, while also giving … (voir plus)rise to misinformation, facilitating and intensifying disinformation campaigns and perpetuating existing patterns of discrimination from the physical world. The first-step platforms take in mitigating the harmful side of social media involves identifying and managing toxic content. Users produce an enormous volume of posts which must be evaluated very quickly. This is an application context that requires machine-learning (ML) tools, but as we detail in this article, ML approaches rely on human annotators, analysts, and moderators. Our review of existing methods and potential improvements indicates that neither humans nor ML can be removed from this process in the near future. However, we see room for improvement in the working conditions of these human workers.

2023-12-01

IEEE Technology and Society Magazine (publié)

doi.org

Toward Stronger Textual Attack Detectors

Pierre Colombo

Marine Picot

Nathan Noiry

Guillaume Staerman

Pablo Piantanida

The landscape of available textual adversarial attacks keeps growing, posing severe threats and raising concerns regarding the deep NLP syst… (voir plus)em's integrity. However, the crucial problem of defending against malicious attacks has only drawn the attention of the NLP community. The latter is nonetheless instrumental in developing robust and trustworthy systems. This paper makes two important contributions in this line of search: (i) we introduce LAROUSSE, a new framework to detect textual adversarial attacks and (ii) we introduce STAKEOUT, a new benchmark composed of nine popular attack methods, three datasets, and two pre-trained models. LAROUSSE is ready-to-use in production as it is unsupervised, hyperparameter-free, and non-differentiable, protecting it against gradient-based methods. Our new benchmark STAKEOUT allows for a robust evaluation framework: we conduct extensive numerical experiments which demonstrate that LAROUSSE outperforms previous methods, and which allows to identify interesting factors of detection rate variations.

2023-12-01

Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)

doi.org

arxiv.org

Twins with psychiatric features and a nonsense HRAS variant affecting transcript processing

Andrea Accogli

Meagan L. Collins Hutchinson

Eric Krochmalnek

Judith St-Onge

Nassima Boudrahem-Addour

Jean-Baptiste Rivière

Ridha Joober

Myriam Srour

Yannis Trakadis

2023-12-01

Psychiatry Research Case Reports (publié)

doi.org

Validation of an AI-assisted Treatment Outcome Measure for Gender-Affirming Voice Care: Comparing AI Accuracy to Listener's Perception of Voice Femininity.

Shane Simon

Einav N. Silverstein

Lauren Timmons-Sund

Jeremy M. Pinto

Eugenia M. Castro

Karla D. O'dell

Michael M. Johns III

Wendy J. Mack

Yael Bensoussan

2023-12-01

Journal of Voice (published)

doi.org

Validation of an AI-assisted Treatment Outcome Measure for Gender-Affirming Voice Care: Comparing AI Accuracy to Listener's Perception of Voice Femininity.

Shane Simon

Einav Silverstein

Lauren Timmons-Sund

Jeremy Pinto

M. Eugenia Castro

Karla O’Dell

Michael M. Johns III

Wendy J. Mack

Yael Bensoussan

2023-12-01

Journal of Voice (publié)

doi.org

XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages

Sebastian Ruder

Jonathan H. Clark

Alexander Gutkin

Mihir Kale

Min Ma

Massimo Nicosia

Shruti Rijhwani

Parker Riley

Jean Michel Amath Sarr

Xinyi Wang

John Frederick Wieting

Nitish Gupta

Anna Katanova

Christo Kirov

Dana L Dickinson

Brian Roark

Bidisha Samanta

Connie Tao

David Ifeoluwa Adelani

Vera Axelrod … (voir 7 de plus)

Isaac Rayburn Caswell

Colin Cherry

Dan Garrette

Reeve Ingle

Melvin Johnson

Dmitry Panteleev

Partha Talukdar

Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) -- l… (voir plus)anguages for which NLP re-search is particularly far behind in meeting user needs -- it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks -- tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text-only, multi-modal (vision, audio, and text),supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models

2023-12-01

Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)

doi.org

openreview.net

Learning domain-invariant classifiers for infant cry sounds

Charles Onu

Hemanth K. Sheetha

Arsenii Gorin

Doina Precup

2023-11-30

ArXiv (prépublication)

arxiv.org

Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem

Nicolas Payot

Mario Pasquato

Alessandro A. Trani

Yashar Hezaveh

Laurence Perreault-Levasseur

Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. Machine learning (ML) is increasingly deployed to pred… (voir plus)ict the evolution of such systems, e.g. with the goal of speeding up simulations. Strategies such as active Learning (AL) are a natural choice to optimize ML training. Here we showcase an AL failure when predicting the stability of the Sitnikov three-body problem, the simplest case of N-body problem displaying chaotic behavior. We link this failure to the fractal nature of our classification problem's decision boundary. This is a potential pitfall in optimizing large sets of N-body simulations via AL in the context of star cluster physics, galactic dynamics, or cosmology.

2023-11-29

ArXiv (prépublication)

arxiv.org

Bayesian Imaging for Radio Interferometry with Score-Based Priors

No'e Dia

M. J. Yantovski-Barth

Alexandre Adam

Micah Bowles

Pablo Lemos

A. Scaife

Yashar Hezaveh

Laurence Perreault-Levasseur

U. Montŕeal

Ciela Institute

Flatiron Institute

2023-11-29

ArXiv (prépublication)

doi.org

arxiv.org

Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors

Alexandre Adam

Connor Stone

Connor Bottrell

Ronan Legin

Yashar Hezaveh

Laurence Perreault-Levasseur

Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant… (voir plus) barriers to such analysis are the non-trivial noise properties of real astronomical images and the point spread function (PSF) which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed \emph{Hubble Space Telescope} (\emph{HST}) data, recovers structures which have otherwise only become visible in next-generation \emph{James Webb Space Telescope} (\emph{JWST}) imaging.

2023-11-29

ArXiv (prépublication)

doi.org

arxiv.org

Extrapolatable Transformer Pre-training for Ultra Long Time-Series Forecasting

Ziyang Song

Qincheng Lu

Hao Xu

He Zhu

David Buckeridge

Yue Li

2023-11-29