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Source language difficulties in learner translation: Evidence from an error-annotated corpus
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Abstract:
This is an accepted manuscript of an article published by John Benjamins Publishing in Target: International Journal of Translation Studies (in press). The accepted version of the publication may differ from the final published version. ; This study uses an error-annotated, mass-media subset of a sentence-aligned, multi-parallel learner translator corpus, to reveal source language items that are challenging in English-to-Russian translation. Our data includes multiple translations to most challenging source sentences, distilled from a large collection of student translations on the basis of error statistics. This sample was subjected to manual contrastive-comparative analysis, which resulted in a list of English items that were difficult to students. The outcome of the analysis was compared to the topics discussed in dozens of translation textbooks that are recommended to BA and specialist-degree students in Russia at the initial stage of professional education. We discuss items that deserve more prominence in training as well as items that call for improvements to traditional learning activities. This study presents evidence that a more empirically-motivated design of practical translation syllabus as part of translator education is required.
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Keyword:
learner corpora; translation difficulties; translation errors; translator education
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URL: http://hdl.handle.net/2436/624403
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An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers ...
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An exploratory analysis of multilingual word-level quality estimation with cross-lingual transformers
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A sequence labelling approach for automatic analysis of ello: tagging pronouns, antecedents, and connective phrases
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TransQuest at WMT2020: Sentence-Level direct assessment
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In: 1049 ; 1055 (2020)
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TransQuest: Translation quality estimation with cross-lingual transformers
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In: 5070 ; 5081 (2020)
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Intelligent translation memory matching and retrieval with sentence encoders
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In: 175 ; 184 (2020)
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Contributions to the Computational Treatment of Non-literal Language
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What matters more: the size of the corpora or their quality? The case of automatic translation of multiword expressions using comparable corpora.
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RGCL at SemEval-2020 task 6: Neural approaches to definition extraction
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In: 717 ; 723 (2020)
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Automated text simplification as a preprocessing step for machine translation into an under-resourced language
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In: Štajner, Sanja orcid:0000-0002-7780-7035 and Popović, Maja orcid:0000-0001-8234-8745 (2019) Automated text simplification as a preprocessing step for machine translation into an under-resourced language. In: Recent Advances in Natural Language Processing (RANLP 2019), 2-4 Sept 2019, Varna, Bulgaria. (2019)
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Are ambiguous conjunctions problematic for machine translation?
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In: Popović, Maja orcid:0000-0001-8234-8745 and Castilho, Sheila orcid:0000-0002-8416-6555 (2019) Are ambiguous conjunctions problematic for machine translation? In: Recent Advances in Natural Language Processing (RANLP 2019), 2 - 4 Sept 2019, Varna, Bulgaria. (2019)
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Natural Language Generation
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In: Handbook of Computational Linguistics (2nd edition) ; https://hal.archives-ouvertes.fr/hal-02079245 ; Mitkov, Ruslan. Handbook of Computational Linguistics (2nd edition), In press (2019)
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Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions ...
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Summary Refinement through Denoising
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In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019) (2019)
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Large-Scale Hierarchical Alignment for Data-driven Text Rewriting
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In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019) (2019)
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Do Online Resources Give Satisfactory Answers to Questions about Meaning and Phraseology?
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RGCL at IDAT: deep learning models for irony detection in Arabic language
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In: 2517 ; 416 ; 425 (2019)
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Bridging the gap: attending to discontinuity in identification of multiword expressions
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