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1
Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian ...
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Quantitative Fine-grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
In: Articles (2018)
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Fine-grained human evaluation of neural versus phrase-based machine translation ...
Abstract: We compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems' outputs. The error types in our annotation are compliant with the multidimensional quality metrics (MQM), and the annotation is performed by two annotators. Inter-annotator agreement is high for such a task, and results show that the best performing system (neural) reduces the errors produced by the worst system (phrase-based) by 54%. ... : 12 pages, 2 figures, The Prague Bulletin of Mathematical Linguistics ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; I.2.7
URL: https://arxiv.org/abs/1706.04389
https://dx.doi.org/10.48550/arxiv.1706.04389
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4
Serbian-English parallel corpus srenWaC 1.0
Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016
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5
Finnish-English parallel corpus fienWaC 1.0
Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016
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6
Tourism English-Croatian Parallel Corpus 2.0
Toral, Antonio; Esplà-Gomis, Miquel; Klubička, Filip. - : Abu-MaTran project, 2016
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7
Croatian-English parallel corpus hrenWaC 2.0
Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016
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8
Slovene-English parallel corpus slenWaC 1.0
Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016
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