1 citations · 1 across the 6 of their papers we have counts for
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Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answering
Patrick Fernandes, Sweta Agrawal, Emmanouil Zaranis +2
Despite the steady progress in machine translation evaluation, existing automatic metrics struggle to capture how well meaning is preserved beyond sentence boundaries. We posit tha…
A Context-aware Framework for Translation-mediated Conversations
José Pombal, Sweta Agrawal, Patrick Fernandes +2
Automatic translation systems offer a powerful solution to bridge language barriers in scenarios where participants do not share a common language. However, these systems can intro…
Analyzing Context Contributions in LLM-based Machine Translation
Emmanouil Zaranis, Nuno M. Guerreiro, André F. T. Martins
Large language models (LLMs) have achieved state-of-the-art performance in machine translation (MT) and demonstrated the ability to leverage in-context learning through few-shot ex…
Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimation
Emmanouil Zaranis, Giuseppe Attanasio, Sweta Agrawal +1
Quality estimation (QE)-the automatic assessment of translation quality-has recently become crucial across several stages of the translation pipeline, from data curation to trainin…
EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments
Emmanouil Zaranis, Georgios Paraskevopoulos, Athanasios Katsamanis +1
In this paper, we introduce EmpBot: an end-to-end empathetic chatbot. Empathetic conversational agents should not only understand what is being discussed, but also acknowledge the…