Automatic Post-Editing of MT Output Using Large Language Models

Blanca Vidal, Albert Llorens, Juan Alonso


Abstract
This presentation will show two experiments conducted to evaluate the adequacy of OpenAI’s GPT-3 (as a representative of Large Language Models), for the purposes of post-editing and translating texts from English into Spanish, using a glossary of terms to ensure term consistency. The experiments are motivated by a use case in ULG MT Production, where we need to improve the usage of terminology glossaries in our NMT system. The purpose of the experiments is to take advantage of GPT-3 outstanding capabilities to generate text for completion and editing. We have used the edits end-point to post-edit the output of a NMT system using a glossary, and the completions end-point to translate the source text, including the glossary term list in the corresponding GPT-3 prompt. While the results are promising, they also show that there is room for improvement by fine-tuning the models, working on prompt engineering, and adjusting the requests parameters.
Anthology ID:
2022.amta-upg.7
Volume:
Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)
Month:
September
Year:
2022
Address:
Orlando, USA
Editors:
Janice Campbell, Stephen Larocca, Jay Marciano, Konstantin Savenkov, Alex Yanishevsky
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
84–106
Language:
URL:
https://aclanthology.org/2022.amta-upg.7
DOI:
Bibkey:
Cite (ACL):
Blanca Vidal, Albert Llorens, and Juan Alonso. 2022. Automatic Post-Editing of MT Output Using Large Language Models. In Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track), pages 84–106, Orlando, USA. Association for Machine Translation in the Americas.
Cite (Informal):
Automatic Post-Editing of MT Output Using Large Language Models (Vidal et al., AMTA 2022)
Copy Citation:
Presentation:
 2022.amta-upg.7.Presentation.pdf