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Findings of the 2017 conference on machine translation (WMT17)
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In: Bojar, Ondřej orcid:0000-0002-0606-0050 , Chatterjee, Rajen, Federmann, Christian, Graham, Yvette, Haddow, Barry, Huang, Shujian, Huck, Matthias, Koehn, Philipp, Liu, Qun orcid:0000-0002-7000-1792 , Logacheva, Varvara, Monz, Christof, Negri, Matteo, Post, Matt, Rubino, Raphael, Specia, Lucia and Turchi, Marco (2017) Findings of the 2017 conference on machine translation (WMT17). In: Second Conference on Machine Translation (WMT17), 7-11 Sept 2017, Copenhagen, Denmark. ISBN 978-1-945626-96-8 (2017)
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SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Cross-lingual Focused Evaluation
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In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) ; The 11th International Workshop on Semantic Evaluation (SemEval-2017) ; https://hal.archives-ouvertes.fr/hal-01560674 ; The 11th International Workshop on Semantic Evaluation (SemEval-2017), Steven Bethard; Marine Carpuat; Marianna Apidianaki; Saif M. Mohammad; Daniel Cer; David Jurgens, Aug 2017, Vancouver, Canada. pp.1 - 14 (2017)
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WMT17 Quality Estimation Shared Task Training and Development Data
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Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description ...
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SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation ...
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MUSST: A Multilingual Syntactic Simplification Tool ...
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Abstract:
We describe MUSST, a multilingual syntactic simplification tool. The tool supports sentence simplifications for English, Italian and Spanish, and can be easily extended to other languages. Our implementation includes a set of general-purpose simplification rules, as well as a sentence selection module (to select sentences to be simplified) and a confidence model (to select only promising simplifications). The tool was implemented in the context of the European project SIMPATICO on text simplification for Public Administration (PA) texts. Our evaluation on sentences in the PA domain shows that we obtain correct simplifications for 76% of the simplified cases in English, 71% of the cases in Spanish. For Italian, the results are lower (38%) but the tool is still under development. ...
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URL: https://dx.doi.org/10.5281/zenodo.1042480 https://zenodo.org/record/1042480
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Complex Word Identification: Challenges in Data Annotation and System Performance ...
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Towards Optimizing MT for Post-Editing Effort: Can BLEU Still Be Useful?
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Modeling context words as regions: an ordinal regression approach to word embedding
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The QT21 Combined Machine Translation System for English to Latvian
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The QT21 combined machine translation system for English to Latvian
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In: 348 ; 357 (2017)
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Bilexical embeddings for quality estimation
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In: 545 ; 550 (2017)
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Guiding neural machine translation decoding with external knowledge
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In: 157 ; 168 (2017)
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Unsupervised, knowledge-free, and interpretable word sense disambiguation
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