Itzulpen automatikoa

Leveraging SNOMED CT terms and relations for machine translation of clinical texts from Basque to Spanish

We present a method for machine translation of clinical texts without using bilingual clinical texts, leveraging the rich terminology and structure of the Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT), which is considered the most comprehensive, multilingual clinical health care terminology collection in the world. We evaluate our method for Basque to Spanish translation, comparing the performance with and without using clinical domain resources.

Neural Machine Translation of clinical texts between long distance languages

ABSTRACT Objective: To analyze techniques for machine translation of electronic health records (EHRs) between long distance languages, using Basque and Spanish as a reference. We studied distinct configurations of neural machine translation systems and used different methods to overcome the lack of a bilingual corpus of clinical texts or health records in Basque and Spanish.

LINGUATEC: Desarrollo de recursos lingüı́sticos para avanzar en la digitalización de las lenguas de los Pirineos

El objetivo del proyecto es desarrollar, probar y difundir nuevos recursos, nuevas herramientas y aplicaciones lingüı́sticas innovadoras para mejorar el nivel de digitalización del aragonés, vasco y occitano.

Adapting NMT to caption translation in Wikimedia Commons for low-resource languages

This paper presents a successful domain adaptation of a general neural machine translation (NMT) system using a bilingual corpus created with captions for images in Wiki- media Commons for the Spanish-Basque and English-Irish pairs. Keywords: Machine Translation, Low-resource languages, Bilingual corpora, Language resources from Wikipedia

The ADAPT System Description for the IWSLT 2018 Basque to English Translation Task

In this paper we present the ADAPT system built for the Basque to English Low Resource MT Evaluation Campaign. Basque is a low-resourced, morphologically-rich language. This poses a challenge for Neural Machine Translation models which usually achieve better performance when trained with large sets of data.


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