Year: 2014
Type: conference-paper
Abstract: This paper summarizes the methods, results and findings of the Discriminating between Similar Languages (DSL) shared task 2014.The shared task provided data from 13 different languages and varieties divided into 6 groups.Participants were required to train their systems to discriminate between languages on a training and development set containing 20,000 sentences from each language (closed submission) and/or any other dataset (open submission).One month later, a test set containing 1,000 unidentified instances per language was released for evaluation.The DSL shared task received 22 inscriptions and 8 final submissions.The best system obtained 95.7% average accuracy.
DOI: 10.3115/v1/w14-5307
Institutions: Saarland University, University of Zagreb, Uppsala University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2162019804