752 citations · 820 across the 21 of their papers we have counts for
7 papers · 1 filter
Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora
Hila Gonen, Ganesh Jawahar, Djamé Seddah +1
The problem of comparing two bodies of text and searching for words that differ in their usage between them arises often in digital humanities and computational social science. Thi…
Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models
José Carlos Rosales Núñez, Guillaume Wisniewski, Djamé Seddah
This work explores the capacities of character-based Neural Machine Translation to translate noisy User-Generated Content (UGC) with a strong focus on exploring the limits of such…
Understanding the Impact of UGC Specificities on Translation Quality
José Carlos Rosales Núñez, Djamé Seddah, Guillaume Wisniewski
This work takes a critical look at the evaluation of user-generated content automatic translation, the well-known specificities of which raise many challenges for MT. Our analyses…
PAGnol: An Extra-Large French Generative Model
Julien Launay, Elena Tommasone, Baptiste Pannier +5
Access to large pre-trained models of varied architectures, in many different languages, is central to the democratization of NLP. We introduce PAGnol, a collection of French GPT m…
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…
Challenging the Semi-Supervised VAE Framework for Text Classification
Ghazi Felhi, Joseph Le Roux, Djamé Seddah
Semi-Supervised Variational Autoencoders (SSVAEs) are widely used models for data efficient learning. In this paper, we question the adequacy of the standard design of sequence SSV…
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
Benjamin Muller, Yanai Elazar, Benoît Sagot +1
Multilingual pretrained language models have demonstrated remarkable zero-shot cross-lingual transfer capabilities. Such transfer emerges by fine-tuning on a task of interest in on…