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1
Identifying effective translations for cross-lingual Arabic-to-English user-generated speech search
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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2
Universal Dependencies 2.1
In: https://hal.inria.fr/hal-01682188 ; 2017 (2017)
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3
Universal Dependencies 2.0 alpha (obsolete)
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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4
Universal Dependencies 2.0
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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5
Universal Dependencies 2.0 – CoNLL 2017 Shared Task Development and Test Data
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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6
Universal Dependencies 2.1
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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7
Low Resourced Machine Translation via Morpho-syntactic Modeling: The Case of Dialectal Arabic ...
Abstract: We present the second ever evaluated Arabic dialect-to-dialect machine translation effort, and the first to leverage external resources beyond a small parallel corpus. The subject has not previously received serious attention due to lack of naturally occurring parallel data; yet its importance is evidenced by dialectal Arabic's wide usage and breadth of inter-dialect variation, comparable to that of Romance languages. Our results suggest that modeling morphology and syntax significantly improves dialect-to-dialect translation, though optimizing such data-sparse models requires consideration of the linguistic differences between dialects and the nature of available data and resources. On a single-reference blind test set where untranslated input scores 6.5 BLEU and a model trained only on parallel data reaches 14.6, pivot techniques and morphosyntactic modeling significantly improve performance to 17.5. ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1712.06273
https://dx.doi.org/10.48550/arxiv.1712.06273
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8
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Çöltekin, Çağrı; Kayadelen, Tolga; Droganova, Kira. - : Association for Computational Linguistics, 2017. : country:USA, 2017. : place:Stroudsburg, PA, 2017
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9
Optimizing Tokenization Choice for Machine Translation across Multiple Target Languages
In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 257-269 (2017) (2017)
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