Artikulua

In 2019, we organized the first iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms: the DISRPT Shared Task on Elementary Discourse Unit Segmentation and Connective Detection. In this paper we review the data included in the task, which cover 2.6 million manually annotated tokens from 15 datasets in 10 languages, survey and compare submit-ted systems and report on system performance on each task for both annotated and plain-tokenized versions of the data.

Lan honek euskararako korreferentzia-ebazpenean egindako lanari jarraipena ematea du helburu, korreferentzia-ebazpenerako neurona-sareetan oinarritutako sistema bat eraikiz. Horretarako polonierarako eraikitako sistema
bat hartu da abiapuntutzat, eta euskarara egokitu. EPEC-KORREF corpusetik abiatuta, aipamen-bikoteak
eta hauen ezaugarriak erauzi dira eta neurona-sarea entrenatu da aipamen-bikoteak korreferenteak ote diren
erabakitzeko. Jarraian, neurona-sarearen iragarpenetatik korreferentzia-klusterrak sortu eta ebaluatu egin dira.

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

Background
Automatic extraction of morbid disease or conditions contained in Death Certificates is a critical process, useful for billing, epidemiological studies and comparison across countries. The fact that these clinical documents are written in regular natural language makes the automatic coding process difficult because, often, spontaneous terms diverge strongly from standard reference terminology such as the International Classification of Diseases (ICD).