Towards a top-down approach for an automatic discourse analysis for Basque: Segmentation and Central Unit detection tool

Lately, discourse structure has received considerable attention due to the benefits carried out by its application in several NLP task such as opinion mining, summarization, question answering, text simplification, among others.

Deep Cross-Lingual Coreference Resolution for Less-ResourcedLanguages: The Case of Basque

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.

The DISRPT 2019 Shared Task on Elementary Discourse UnitSegmentation and Connective Detection

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.


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