2 citations · 3 across the 5 of their papers we have counts for
5 papers · 1 filter
The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMs
Aleix Sant, Carlos Escolano, Audrey Mash +2
This paper studies gender bias in machine translation through the lens of Large Language Models (LLMs). Four widely-used test sets are employed to benchmark various base LLMs, comp…
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…
Promoting Generalized Cross-lingual Question Answering in Few-resource Scenarios via Self-knowledge Distillation
Casimiro Pio Carrino, Carlos Escolano, José A. R. Fonollosa
Despite substantial progress in multilingual extractive Question Answering (QA), models with high and uniformly distributed performance across languages remain challenging, especia…
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…
Morphology Generation for Statistical Machine Translation using Deep Learning Techniques
Marta R. Costa-jussà, Carlos Escolano
Morphology in unbalanced languages remains a big challenge in the context of machine translation. In this paper, we propose to de-couple machine translation from morphology generat…