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    • Toral, Antonio (8)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian ... Klubička, Filip; Toral, Antonio; Sánchez-Cartagena, Víctor M.. - : arXiv, 2018 BASE Show details 2 Quantitative Fine-grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian Sanchez-Cartagena, Victor Manuel; Toral, Antonio; Klubicka, Filip In: Articles (2018) Abstract: This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established Multidimensional Quality Metrics (MQM) error taxonomy and implement a novel method that assesses whether the differences in performance for MQM error types between different MT systems are statistically significant. We conduct a case study for English-to- Croatian, a language direction that involves translating into a morphologically rich language, for which we compare three MT systems belonging to different paradigms: pure phrase-based, factored phrase-based and neural. First, we design an MQM-compliant error taxonomy tailored to the relevant linguistic phenomena of Slavic languages, which made the annotation process feasible and accurate. Errors in MT outputs were then annotated by two annotators following this taxonomy. Subsequently, we carried out a statistical analysis which showed that the best-performing system (neural) reduces the errors produced by the worst system (pure phrase-based) by more than half (54%). Moreover, we conducted an additional analysis of agreement errors in which we distinguished between short (phrase-level) and long distance (sentence-level) errors. We discovered that phrase-based MT approaches are of limited use for long distance agreement phenomena, for which neural MT was found to be especially effective. Keyword: Computational Engineering; Digital Humanities; error annotation; factored models; human evaluation; Language Interpretation and Translation; Modern Languages; multidimensional quality metrics (MQM); neural machine translation; Other Computer Engineering; phrase-based machine translation; Slavic Languages and Societies; statistical machine translation URL: https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1062&context=scschcomart https://arrow.tudublin.ie/scschcomart/57 BASE Hide details 3 Fine-grained human evaluation of neural versus phrase-based machine translation ... Klubička, Filip; Toral, Antonio; Sánchez-Cartagena, Víctor M.. - : arXiv, 2017 BASE Show details 4 Serbian-English parallel corpus srenWaC 1.0 Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016 BASE Show details 5 Finnish-English parallel corpus fienWaC 1.0 Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016 BASE Show details 6 Tourism English-Croatian Parallel Corpus 2.0 Toral, Antonio; Esplà-Gomis, Miquel; Klubička, Filip. - : Abu-MaTran project, 2016 BASE Show details 7 Croatian-English parallel corpus hrenWaC 2.0 Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016 BASE Show details 8 Slovene-English parallel corpus slenWaC 1.0 Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016 BASE Show details

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