Optimizing Machine Translation Models: Practical Strategies with OpenNMT

Martín Sujo, Jessie; Vilasis-Cardona, Xavier

dc.contributor	Universitat Ramon Llull. La Salle
dc.contributor.author	Martín Sujo, Jessie
dc.contributor.author	Vilasis-Cardona, Xavier
dc.date.accessioned	2026-02-26T15:50:32Z
dc.date.available	2026-02-26T15:50:32Z
dc.date.created	2025-10
dc.date.issued	2025-10
dc.identifier.issn	1879-8314	ca
dc.identifier.uri	http://hdl.handle.net/20.500.14342/5990
dc.description.abstract	In correspondence with the advancement of Natural Language Processing (NLP), the field of Translation has also experienced significant advances, for example, with the use of an ecosystem for neural machine translation, called OpenNMT. However, it still has limitations, especially when it comes to translating much more specific texts. That is why this study focuses on the practical strategies that developers should follow to obtain more accurate results.	ca
dc.format.extent	7 p.	ca
dc.language.iso	eng	ca
dc.publisher	IOS Press	ca
dc.relation.ispartof	Artificial Intelligence Research and Development - Proceedings of the 27th International Conference of the Catalan Association for Artificial Intelligence	ca
dc.rights	© L'autor/a	ca
dc.rights	Attribution-NonCommercial 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc/4.0/	*
dc.subject.other	Natural language procesing	ca
dc.subject.other	Translation	ca
dc.subject.other	OpenNMT	ca
dc.title	Optimizing Machine Translation Models: Practical Strategies with OpenNMT	ca
dc.type	info:eu-repo/semantics/article	ca
dc.rights.accessLevel	info:eu-repo/semantics/openAccess
dc.embargo.terms	cap	ca
dc.subject.udc	004	ca
dc.subject.udc	62	ca
dc.subject.udc	8	ca
dc.identifier.doi	http://doi.org/10.3233/FAIA250582	ca
dc.description.version	info:eu-repo/semantics/publishedVersion	ca