17 citations · 48 across the 27 of their papers we have counts for
8 papers · 1 filter
The Roles of English in Evaluating Multilingual Language Models
Wessel Poelman, Miryam de Lhoneux
Multilingual natural language processing is getting increased attention, with numerous models, benchmarks, and methods being released for many languages. English is often used in m…
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…
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…
Recipe for Zero-shot POS Tagging: Is It Useful in Realistic Scenarios?
Zeno Vandenbulcke, Lukas Vermeire, Miryam de Lhoneux
POS tagging plays a fundamental role in numerous applications. While POS taggers are highly accurate in well-resourced settings, they lag behind in cases of limited or missing trai…
Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP
François Remy, Pieter Delobelle, Hayastan Avetisyan +3
The development of monolingual language models for low and mid-resource languages continues to be hindered by the difficulty in sourcing high-quality training data. In this study,…
A Principled Framework for Evaluating on Typologically Diverse Languages
Esther Ploeger, Wessel Poelman, Andreas Holck Høeg-Petersen +3
Beyond individual languages, multilingual natural language processing (NLP) research increasingly aims to develop models that perform well across languages generally. However, eval…
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…
What is "Typological Diversity" in NLP?
Esther Ploeger, Wessel Poelman, Miryam de Lhoneux +1
The NLP research community has devoted increased attention to languages beyond English, resulting in considerable improvements for multilingual NLP. However, these improvements onl…