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1
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer ...
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Unsupervised Translation of German--Lower Sorbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language ...
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Generic resources are what you need: Style transfer tasks without task-specific parallel training data ...
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4
A set of recommendations for assessing human-machine parity in language translation
In: Läubli, Samuel orcid:0000-0001-5362-4106 , Castilho, Sheila orcid:0000-0002-8416-6555 , Neubig, Graham, Sennrich, Rico orcid:0000-0002-1438-4741 , Shen, Qinlan and Toral, Antonio orcid:0000-0003-2357-2960 (2020) A set of recommendations for assessing human-machine parity in language translation. Journal of Artificial Intelligence Research, 67 . pp. 653-672. ISSN 1076-9757 (2020)
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Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT ...
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A Set of Recommendations for Assessing Human-Machine Parity in Language Translation ...
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7
Editors’ foreword to the special issue on human factors in neural machine translation [<Journal>]
Castilho, Sheila [Verfasser]; Gaspari, Federico [Verfasser]; Moorkens, Joss [Verfasser].
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8
Post-editing effort of a novel with statistical and neural machine translation
In: Toral, Antonio, Wieling, Martijn and Way, Andy orcid:0000-0001-5736-5930 (2018) Post-editing effort of a novel with statistical and neural machine translation. Frontiers in Digital Humanities, 5 (9). pp. 1-11. ISSN 2297-2668 (2018)
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9
What level of quality can neural machine translation attain on literary text?
In: Toral, Antonio orcid:0000-0003-2357-2960 and Way, Andy orcid:0000-0001-5736-5930 (2018) What level of quality can neural machine translation attain on literary text? In: Moorkens, Joss orcid:0000-0003-4864-5986 , Castilho, Sheila orcid:0000-0002-8416-6555 , Gaspari, Federico orcid:0000-0003-3808-8418 and Doherty, S, (eds.) Translation Quality Assessment: From Principles to Practice. Machine Translation: Technologies and Applications book series (MATRA), 1 . Springer, Berlin/Heidelberg, 263 -287. ISBN 978-3-319-91240-0 (2018)
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10
Post-editing effort of a novel with statistical and neural machine translation
In: Toral, Antonio orcid:0000-0003-2357-2960 , Wieling, Martijn orcid:0000-0003-0434-1526 and Way, Andy orcid:0000-0001-5736-5930 (2018) Post-editing effort of a novel with statistical and neural machine translation. Frontiers in Digital Humanities, 5 . ISSN 2297-2668 (2018)
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11
Attaining the unattainable? Reassessing claims of human parity in neural machine translation
In: Toral, Antonio orcid:0000-0003-2357-2960 , Castilho, Sheila orcid:0000-0002-8416-6555 , Hu, Ke and Way, Andy orcid:0000-0001-5736-5930 (2018) Attaining the unattainable? Reassessing claims of human parity in neural machine translation. In: Third Conference on Machine Translation (WMT), 31 Oct- 1 Nov 2018, Brussels, Belgium. ISBN 978-1-948087-81-0 (2018)
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12
Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian ...
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13
Quantitative Fine-grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
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
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Fine-grained human evaluation of neural versus phrase-based machine translation ...
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A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions ...
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Fine-Grained Human Evaluation of Neural Versus Phrase-Based Machine Translation
In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 121-132 (2017) (2017)
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17
CloudLM: a cloud-based language model for machine translation
In: Ferrández-Tordera, Jorge, Ortiz-Rojas, Sergio and Toral, Antonio orcid:0000-0003-2357-2960 (2016) CloudLM: a cloud-based language model for machine translation. Prague Bulletin of Mathematical Linguistics (105). pp. 51-61. ISSN 1804-0462 (2016)
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18
Serbian-English parallel corpus srenWaC 1.0
Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016
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Finnish web corpus fiWaC 1.0
Ljubešić, Nikola; Pirinen, Tommi; Toral, Antonio. - : Jožef Stefan Institute, 2016
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Finnish-English parallel corpus fienWaC 1.0
Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016
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