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cs.CL2024
How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP
Kushal Tatariya, Artur Kulmizev, Wessel Poelman +6
Wikipedia's perceived high quality and broad language coverage have established it as a fundamental resource in NLP. However, in recent years, such assumptions of high quality have…
cs.CL2024
Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models
Kushal Tatariya, Vladimir Araujo, Thomas Bauwens +1
Pixel-based language models have emerged as a compelling alternative to subword-based language modelling, particularly because they can represent virtually any script. PIXEL, a can…
cs.CL2024
Sociolinguistically Informed Interpretability: A Case Study on Hinglish Emotion Classification
Kushal Tatariya, Heather Lent, Johannes Bjerva +1
Emotion classification is a challenging task in NLP due to the inherent idiosyncratic and subjective nature of linguistic expression, especially with code-mixed data. Pre-trained l…