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How much context span is enough? Examining context-related issues for document-level MT
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In: Castilho, Sheila orcid:0000-0002-8416-6555 (2022) How much context span is enough? Examining context-related issues for document-level MT. In: 13th Language Resources and Evaluation Conference, 21-23 June 2022, Marseille, France. (In Press) (2022)
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DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues
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In: Castilho, Sheila orcid:0000-0002-8416-6555 , Cavalheiro Camargo, João Lucas orcid:0000-0003-3746-1225 , Menezes, Miguel and Way, Andy orcid:0000-0001-5736-5930 (2021) DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues. In: Sixth Conference on Machine Translation (WMT21), 10-11 Nov 2021, Punta Cana, Dominican Republic (Online). ISBN 978-1-954085-94-7 (2021)
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Towards document-level human MT evaluation: On the Issues of annotator agreement, effort and misevaluation
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In: Castilho, Sheila orcid:0000-0002-8416-6555 (2021) Towards document-level human MT evaluation: On the Issues of annotator agreement, effort and misevaluation. In: 16th Conference of the European Chapter of the Association for Computational Linguistics - EACL 2021., 19-23 April 2021, Online. (In Press) (2021)
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A human evaluation of English-Irish statistical and neural machine translation
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In: Dowling, Meghan orcid:0000-0003-1637-4923 , Castilho, Sheila orcid:0000-0002-8416-6555 , Moorkens, Joss orcid:0000-0003-4864-5986 , Lynn, Teresa and Way, Andy orcid:0000-0001-5736-5930 (2020) A human evaluation of English-Irish statistical and neural machine translation. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, 6 Nov 2020, Lisbon, Portugal. (2020)
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A set of recommendations for assessing human-machine parity in language translation
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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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Abstract:
The quality of machine translation has increased remarkably over the past years, to the degree that it was found to be indistinguishable from professional human translation in a number of empirical investigations. We reassess Hassan et al.’s 2018 investigation into Chinese to English news translation, showing that the finding of human–machine parity was owed to weaknesses in the evaluation design—which is currently considered best practice in the field. We show that the professional human translations contained significantly fewer errors, and that perceived quality in human evaluation depends on the choice of raters, the availability of linguistic context, and the creation of reference translations. Our results call for revisiting current best practices to assess strong machine translation systems in general and human–machine parity in particular, for which we offer a set of recommendations based on our empirical findings.
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Keyword:
human evaluation of machine translation; Machine translating; Translating and interpreting
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URL: http://doras.dcu.ie/24536/
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On the same page? Comparing inter-annotator agreement in sentence and document level human machine translation evaluation
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In: Castilho, Sheila orcid:0000-0002-8416-6555 (2020) On the same page? Comparing inter-annotator agreement in sentence and document level human machine translation evaluation. In: Fifth Conference on Machine Translation, 19-20 Nov 2020, Dominican Republic (Online). (2020)
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Measuring acceptability of machine translated enterprise content
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Castilho, Sheila. - : Dublin City University. Faculty of Humanities and Social Science, 2016. : Dublin City University. School of Applied Language and Intercultural Studies, 2016. : Dublin City University. ADAPT, 2016
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In: Castilho, Sheila orcid:0000-0002-8416-6555 (2016) Measuring acceptability of machine translated enterprise content. PhD thesis, Dublin City University. (2016)
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