Skip to Main Content (Press Enter)

Logo UNIOR
  • ×
  • Home
  • Corsi
  • Insegnamenti
  • Persone
  • Strutture

UNIFIND
Logo UNIOR

|

UNIFIND

unior.it
  • ×
  • Home
  • Corsi
  • Insegnamenti
  • Persone
  • Strutture

The SETU-ADAPT Submission for WMT 24 Biomedical Shared Task

Contributo in Atti di convegno
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
Autori di Ateneo:
MONTI JOHANNA
Link alla scheda completa:
https://unora.unior.it/handle/11574/237261
Link al Full Text:
https://unora.unior.it//retrieve/handle/11574/237261/191075/2024.wmt-1.53.pdf
Titolo del libro:
Proceedings of the Ninth Conference on Machine Translation
  • Dati Generali

Dati Generali

URL

https://aclanthology.org/2024.wmt-1.53.pdf
  • Utilizzo dei cookie

Realizzato con VIVO | Designed by Cineca | 26.5.2.0