Findings of the VarDial Evaluation Campaign 2017
Marcos Zampieri,Shervin Malmasi,Nikola Ljubešić,Preslav Nakov,Ahmed Ali,Jörg Tiedemann,Yves Scherrer,Noëmi Aepli +7 more
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TLDR
The VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects, which was organized as part of the fourth edition of the VarDial workshop at EACL’2017, is presented.Abstract:
We present the results of the VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects, which we organized as part of the fourth edition of the VarDial workshop at EACL’2017 This year, we included four shared tasks: Discriminating between Similar Languages (DSL), Arabic Dialect Identification (ADI), German Dialect Identification (GDI), and Cross-lingual Dependency Parsing (CLP) A total of 19 teams submitted runs across the four tasks, and 15 of them wrote system description papersread more
Citations
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Proceedings ArticleDOI
Identifying dialects with textual and acoustic cues
TL;DR: Several systems for identifying short samples of Arabic or Swiss-German dialects were prepared for the shared task of the 2017 DSL Workshop, and the best runs achieved a accuracy of nearly 63% on both the Swiss- German and Arabic dialects tasks.
Proceedings Article
Iterative Language Model Adaptation for Indo-Aryan Language Identification
TL;DR: The SUKI team's submission using a HeLI-method based language identifier with iterative language model adaptation obtained the best results in the shared task with a macro F1-score of 0.958.
Posted Content
The Unreasonable Effectiveness of Machine Learning in Moldavian versus Romanian Dialect Identification
Mihaela Gaman,Radu Tudor Ionescu +1 more
TL;DR: A subjective evaluation by human annotators, showing that humans attain much lower accuracy rates compared to machine learning (ML) models, and experiments showing that the machine learning performance on the MRC shared task can be improved through an ensemble based on classifier stacking.
Proceedings ArticleDOI
CLUZH at VarDial GDI 2017: Testing a Variety of Machine Learning Tools for the Classification of Swiss German Dialects
Simon Clematide,Peter Makarov +1 more
TL;DR: The submissions for the GDI 2017 Shared Task are the results from three different types of classifiers: Naïve Bayes, Conditional Random Fields (CRF), and Support Vector Machine (SVM).
Findings of the VarDial Evaluation Campaign 2022
Noëmi Aepli,Antonios Anastasopoulos,Adrian-Gabriel Chifu,William Domingues,Fahim Faisal,Mihaela Gaman,Radu Ionescu,Yves Scherrer +7 more
TL;DR: The results of the shared tasks organized as part of the VarDial Evaluation Campaign 2022 are presented in this paper , where three separate shared tasks are included: identification of languages and dialects of Italy (ITDI), French Cross-Domain Dialect Identification (FDI), and Dialectal Extractive Question Answering (DialQA).
References
More filters
Proceedings Article
Okapi at TREC
TL;DR: Much of the work involved investigating plausible methods of applying Okapi-style weighting to phrases, and expansion using terms from the top documents retrieved by a pilot search on topic terms was used.
Proceedings Article
Parallel Data, Tools and Interfaces in OPUS
TL;DR: New data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the OPUS project are reported.
Proceedings Article
Universal Dependencies v1: A Multilingual Treebank Collection
Joakim Nivre,Marie-Catherine de Marneffe,Filip Ginter,Yoav Goldberg,Jan Hajič,Christopher D. Manning,Ryan McDonald,Slav Petrov,Sampo Pyysalo,Natalia Silveira,Reut Tsarfaty,Daniel Zeman +11 more
TL;DR: This paper describes v1 of the universal guidelines, the underlying design principles, and the currently available treebanks for 33 languages, as well as highlighting the needs for sound comparative evaluation and cross-lingual learning experiments.
Proceedings Article
Universal Dependency Annotation for Multilingual Parsing
Ryan McDonald,Joakim Nivre,Yvonne Quirmbach-Brundage,Yoav Goldberg,Dipanjan Das,Kuzman Ganchev,Keith Hall,Slav Petrov,Hao Zhang,Oscar Täckström,Claudia Bedini,Núria Bertomeu Castelló,Jungmee Lee +12 more
TL;DR: A new collection of treebanks with homogeneous syntactic dependency annotation for six languages: German, English, Swedish, Spanish, French and Korean is presented, made freely available in order to facilitate research on multilingual dependency parsing.
Journal ArticleDOI
Bootstrapping parsers via syntactic projection across parallel texts
TL;DR: Using parallel text to help solving the problem of creating syntactic annotation in more languages by annotating the English side of a parallel corpus, project the analysis to the second language, and train a stochastic analyzer on the resulting noisy annotations.