Paper

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.

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.

In this paper, we present a cross-lingual neural coreference resolution system for a less-resourced language such as Basque. To begin with, we build the first neural coreferenceresolution system for Basque, training it with the relatively small EPEC-KORREF corpus (45,000 words). Next, a cross-lingual coreference resolution system is designed. With this approach, the system learns from a bigger English corpus, using cross-lingual embeddings, to perform the coreference resolution for Basque.