Juan Alonso


Automatic Post-Editing of MT Output Using Large Language Models
Blanca Vidal | Albert Llorens | Juan Alonso
Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)

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.


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Leveraging Rule-Based Machine Translation Knowledge for Under-Resourced Neural Machine Translation Models
Daniel Torregrosa | Nivranshu Pasricha | Maraim Masoud | Bharathi Raja Chakravarthi | Juan Alonso | Noe Casas | Mihael Arcan
Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks