1 citations · 1 across the 3 of their papers we have counts for
3 papers
MT-LENS: An all-in-one Toolkit for Better Machine Translation Evaluation
Javier García Gilabert, Carlos Escolano, Audrey Mash +2
We introduce MT-LENS, a framework designed to evaluate Machine Translation (MT) systems across a variety of tasks, including translation quality, gender bias detection, added toxic…
Investigating the translation capabilities of Large Language Models trained on parallel data only
Javier García Gilabert, Carlos Escolano, Aleix Sant Savall +4
In recent years, Large Language Models (LLMs) have demonstrated exceptional proficiency across a broad spectrum of Natural Language Processing (NLP) tasks, including Machine Transl…
ReSeTOX: Re-learning attention weights for toxicity mitigation in machine translation
Javier García Gilabert, Carlos Escolano, Marta R. Costa-Jussà
Our proposed method, ReSeTOX (REdo SEarch if TOXic), addresses the issue of Neural Machine Translation (NMT) generating translation outputs that contain toxic words not present in…