27 citations · 31 across the 7 of their papers we have counts for
9 papers
IYKYK: Using language models to decode extremist cryptolects
Christine de Kock, Arij Riabi, Zeerak Talat +3
Extremist groups develop complex in-group language, also referred to as cryptolects, to exclude or mislead outsiders. We investigate the ability of current language technologies to…
Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection
Arij Riabi, Virginie Mouilleron, Menel Mahamdi +2
The proliferation of radical content on online platforms poses significant risks, including inciting violence and spreading extremist ideologies. Despite ongoing research, existing…
Common Ground, Diverse Roots: The Difficulty of Classifying Common Examples in Spanish Varieties
Javier A. Lopetegui, Arij Riabi, Djamé Seddah
Variations in languages across geographic regions or cultures are crucial to address to avoid biases in NLP systems designed for culturally sensitive tasks, such as hate speech det…
Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks
Arij Riabi, Menel Mahamdi, Virginie Mouilleron +1
Protecting privacy is essential when sharing data, particularly in the case of an online radicalization dataset that may contain personal information. In this paper, we explore the…
Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark
Stephen Mayhew, Terra Blevins, Shuheng Liu +10
We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages. The overarching goal of UNER is to provide high-qual…
Enriching the NArabizi Treebank: A Multifaceted Approach to Supporting an Under-Resourced Language
Arij Riabi, Menel Mahamdi, Djamé Seddah
In this paper we address the scarcity of annotated data for NArabizi, a Romanized form of North African Arabic used mostly on social media, which poses challenges for Natural Langu…
Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Models
Syrielle Montariol, Arij Riabi, Djamé Seddah
Zero-shot cross-lingual transfer learning has been shown to be highly challenging for tasks involving a lot of linguistic specificities or when a cultural gap is present between la…
Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
Arij Riabi, Benoît Sagot, Djamé Seddah
Recent impressive improvements in NLP, largely based on the success of contextual neural language models, have been mostly demonstrated on at most a couple dozen high-resource lang…
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
Arij Riabi, Thomas Scialom, Rachel Keraron +3
Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task. However, most of those datasets are i…