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.

The SETU-ADAPT Submission for WMT 24 Biomedical Shared Task

Castaldo, Antonio;Monti, Johanna
2024-01-01

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.
2024
Inglese
AA.VV
Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
Proceedings of the Ninth Conference on Machine Translation
contributo
Ninth Conference on Machine Translation
647
653
7
https://aclanthology.org/2024.wmt-1.53.pdf
Association for Computational Linguistics
Esperti anonimi
November 15-16, 2024
Miami, Florida, USA
Internazionale
Large Language Models, Machine Translation, terminology, biomedical domain
6
Castaldo, Antonio; Zafar, Maria; Nayak, Prashanth; Haque, Rejwanul; Way, Andy; Monti, Johanna
open
273
info:eu-repo/semantics/conferenceObject
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11574/237261
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