Djamé Seddah

Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models

2021 · arXiv (Cornell University)

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 approaches to handle productive UGC phenomena, which almost by …

Integrating Semantic Representations in a Cross-Modal Approach to Fact-Checking

2025

We propose a cross-modal approach with deep fusion of a language model and graph structures based on Abstract Meaning Representations (AMRs) enriched with Wikidata to address the fact-checking problem.We collect and make available a large …

What Does BERT Learn about the Structure of Language?

2019

BERT is a recent language representation model that has surprisingly performed well in diverse language understanding benchmarks. This result indicates the possibility that BERT networks capture structural information about language. In this work, we provide …

CamemBERT: a Tasty French Language Model

2020

Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric de la Clergerie, Djamé Seddah, Benoît Sagot. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.