Data di Pubblicazione:
2024
Abstract:
This system description paper presents SETU-ADAPT’s submission to the WMT 2024 Biomedical Shared Task, where we participated for the language pairs English-to-French and English-to-German. Our approach focused on fine-tuning Large Language Models, using in-domain and synthetic data, employing different data augmentation and data retrieval strategies. We introduce a novel MT framework, involving three autonomous agents: a Translator Agent, an Evaluator Agent and a Reviewer Agent. We present our findings and report the quality of the outputs.
Tipologia CRIS:
4.1 Contributo in Atti di convegno
Keywords:
Large Language Models, Machine Translation, terminology, biomedical domain
Elenco autori:
Castaldo, Antonio; Zafar, Maria; Nayak, Prashanth; Haque, Rejwanul; Way, Andy; Monti, Johanna
Link alla scheda completa:
Titolo del libro:
Proceedings of the Ninth Conference on Machine Translation