Biomedical Parallel Sentence Retrieval Using Large Language Models

Sheema Firdous, Sadaf Abdul Rauf


Abstract
We have explored the effect of in domain knowledge during parallel sentence filtering from in domain corpora. Models built with sentences mined from in domain corpora without domain knowledge performed poorly, whereas model performance improved by more than 2.3 BLEU points on average with further domain centric filtering. We have used Large Language Models for selecting similar and domain aligned sentences. Our experiments show the importance of inclusion of domain knowledge in sentence selection methodologies even if the initial comparable corpora are in domain.
Anthology ID:
2023.wmt-1.26
Volume:
Proceedings of the Eighth Conference on Machine Translation
Month:
December
Year:
2023
Address:
Singapore
Editors:
Philipp Koehn, Barry Haddow, Tom Kocmi, Christof Monz
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
263–270
Language:
URL:
https://aclanthology.org/2023.wmt-1.26
DOI:
10.18653/v1/2023.wmt-1.26
Bibkey:
Cite (ACL):
Sheema Firdous and Sadaf Abdul Rauf. 2023. Biomedical Parallel Sentence Retrieval Using Large Language Models. In Proceedings of the Eighth Conference on Machine Translation, pages 263–270, Singapore. Association for Computational Linguistics.
Cite (Informal):
Biomedical Parallel Sentence Retrieval Using Large Language Models (Firdous & Rauf, WMT 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.wmt-1.26.pdf