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Found 10 papers, 3 papers with code
Can Character-based Language Models Improve Downstream Task Performances In Low-Resource And Noisy Language Scenarios?
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 languages.
Fine-tuning and Sampling Strategies for Multimodal Role Labeling of Entities under Class Imbalance
We propose our solution to the multimodal semantic role labeling task from the CONSTRAINT’22 workshop.
Tâches Auxiliaires Multilingues pour le Transfert de Modèles de Détection de Discours Haineux (Multilingual Auxiliary Tasks for Zero-Shot Cross-Lingual Transfer of Hate Speech Detection)
La tâche de détection de contenus haineux est ardue, car elle nécessite des connaissances culturelles et contextuelles approfondies ; les connaissances nécessaires varient, entre autres, selon la langue du locateur ou la cible du contenu.
Common Ground, Diverse Roots: The Difficulty of Classifying Common Examples in Spanish Varieties
To our knowledge, this is the first dataset focused on identifying the Cuban, or any other Caribbean, Spanish variety.
Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection
The proliferation of radical content on online platforms poses significant risks, including inciting violence and spreading extremist ideologies.
Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks
Protecting privacy is essential when sharing data, particularly in the case of an online radicalization dataset that may contain personal information.
Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark
We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages.
Ranked #1 on
Named Entity Recognition (NER)
on UNER v1 (Danish)
Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Models
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 languages, such as in hate speech detection.
Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
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 languages.
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task.
