collaborators

5 papers

cs.CL2026

CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data

Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett +94

Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, especially on the noisy and heter…

cs.CL2026

How Should We Model the Probability of a Language?

Rasul Dent, Pedro Ortiz Suarez, Thibault Clérice +1

Of the over 7,000 languages spoken in the world, commercial language identification (LID) systems only reliably identify a few hundred in written form. Research-grade systems exten…

cs.CL2025

Low-Resource, High-Impact: Building Corpora for Inclusive Language Technologies

Ekaterina Artemova, Laurie Burchell, Daryna Dementieva +3

This tutorial (https://tum-nlp.github.io/low-resource-tutorial) is designed for NLP practitioners, researchers, and developers working with multilingual and low-resource languages…

cs.CL2025

mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus

Matthieu Futeral, Armel Zebaze, Pedro Ortiz Suarez +5

Multimodal Large Language Models (mLLMs) are trained on a large amount of text-image data. While most mLLMs are trained on caption-like data only, Alayrac et al. (2022) showed that…

cs.CL2025

KréyoLID From Language Identification Towards Language Mining

Rasul Dent, Pedro Ortiz Suarez, Thibault Clérice +1

Automatic language identification is frequently framed as a multi-class classification problem. However, when creating digital corpora for less commonly written languages, it may b…