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Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling ...
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Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging ...
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CoNLL-UL: Universal Morphological Lattices for Universal Dependency Parsing
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In: 11th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-01786125 ; 11th Language Resources and Evaluation Conference, May 2018, Miyazaki, Japan ; http://lrec2018.lrec-conf.org (2018)
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Universal Dependencies 2.2
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In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction ...
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Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models ...
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Identifying effective translations for cross-lingual Arabic-to-English user-generated speech search
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In: Khwileh, Ahmad, Afli, Haithem orcid:0000-0002-7449-4707 , Jones, Gareth J.F. orcid:0000-0003-2923-8365 and Way, Andy orcid:0000-0001-5736-5930 (2017) Identifying effective translations for cross-lingual Arabic-to-English user-generated speech search. In: Third Arabic Natural Language Processing Workshop (WANLP), 3 Apr 2017, Valencia, Spain. (2017)
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Universal Dependencies 2.1
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In: https://hal.inria.fr/hal-01682188 ; 2017 (2017)
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Universal Dependencies 2.0 – CoNLL 2017 Shared Task Development and Test Data
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Low Resourced Machine Translation via Morpho-syntactic Modeling: The Case of Dialectal Arabic ...
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CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
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Çöltekin, Çağrı; Kayadelen, Tolga; Droganova, Kira; Gokirmak, Memduh; Reddy, Siva; Kettnerová, Václava; Popel, Martin; Nitisaroj, Rattima; Leung, Herman; Pyysalo, Sampo; Ginter, Filip; Missilä, Anna; dePaiva, Valeria; Martínez Alonso, Héctor; Habash, Nizar; Banerjee, Esha; Tyers, Francis; Sanguinetti, Manuela; Lertpradit, Saran; Uszkoreit, Hans; Yu, Zhuoran; Li, Josie; Sulubacak, Umut; Simi, Maria; Harris, Kim; de Marneffe, Marie-Catherine; Burchardt, Aljoscha; Straka, Milan; Kanayama, Hiroshi; Rehm, Georg; Hlavacova, Jaroslava; Attia, Mohammed; Elkahky, Ali; Kwak, Sookyoung; Kanerva, Jenna; Nedoluzhko, Anna; Marheinecke, Katrin; Stella, Antonio; Lando, Tatiana; Badmaeva, Elena; Uresova, Zdenka; Hajic, Jan; Pitler, Emily; Potthast, Martin; Cinkova, Silvie; Ojala, Stina; Strnadová, Jana; Taji, Dima; Manning, Christopher D.; Mandl, Michael; Kirchner, Jesse; Mendonca, Gustavo; Alcalde, Hector Fernandez; Schuster, Sebastian; Petrov, Slav; Zeman, Daniel; Shimada, Atsuko; Nivre, Joakim; Luotolahti, Juhani; Hajic jr., Jan; Manurung, Ruli; Macketanz, Vivien. - : Association for Computational Linguistics, 2017. : country:USA, 2017. : place:Stroudsburg, PA, 2017
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Abstract:
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, one of two tasks was devoted to learning dependency parsers for a large number of languages, in a realworld setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe data preparation, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.
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Keyword:
dependency syntax; evaluation; parsing; Universal Dependencies
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URL: http://www.aclweb.org/anthology/K/K17/K17-3001.pdf https://doi.org/10.18653/v1/K17-3001 http://hdl.handle.net/2318/1652589
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Optimizing Tokenization Choice for Machine Translation across Multiple Target Languages
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 257-269 (2017) (2017)
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