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1
Translation quality assessment: a brief survey on manual and automatic methods
In: Han, Lifeng orcid:0000-0002-3221-2185 , Jones, Gareth J.F. orcid:0000-0003-2923-8365 and Smeaton, Alan F. orcid:0000-0003-1028-8389 (2021) Translation quality assessment: a brief survey on manual and automatic methods. In: MoTra21: Workshop on Modelling Translation: Translatology in the Digital Age, 31 May- 2 Jun 2021, Rejkjavik, Iceland (Online). (In Press) (2021)
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2
Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement
In: Alekseeva, Alexandra orcid:0000-0002-7990-4592 , Gladkoff, Serge, Sorokina, Irina and Han, Lifeng orcid:0000-0002-3221-2185 (2021) Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement. In: Metrics 2021: Workshop on Informetric and Scientometric Research (SIG-MET), 23-24 Oct 2021, Online. (2021)
Abstract: From both human translators (HT) and machine translation (MT) researchers' point of view, translation quality evaluation (TQE) is an essential task. This is especially the case, when language service providers (LSPs) face huge amount of request frequently from their clients and users to acquire high-quality translations. While automatic translation quality assessment (TQA) metrics and quality estimation (QE) tools are widely available and easy to access, human assessment from professional translators (HAP) are often chosen as the golden standard \cite{han-etal-2021-TQA}. One challenge that comes to this point is this: \textit{to avoid the overall text quality checking from both cost and efficiency perspectives, how to choose the confidence sample size of the translated text, so as to properly estimate the overall text or document translation quality}? This work carries out such an motivated research to correctly estimate the confidence intervals \cite{Brown_etal2001Interval} regarding the sample size of translated text, e.g. the amount of words or sentences, that needs to be taken into account for confident evaluation of overall translation quality. The methodology we applied for this work is from Bernoulli Statistical Distribution Modelling (BSDM) and Monte Carlo Sampling Analysis (MCSA).
Keyword: Algorithms; Artificial intelligence; Computational linguistics; Computer software; Information technology; Post-editing Distance; Confidence Intervals; Monte Carlo Modeling; Bernoulli Statistics; Probabilities; Quality Estimation; Statistics; Stochastic analysis; Translation Quality Evaluation
URL: http://doras.dcu.ie/26281/
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3
Meta-evaluation of machine translation evaluation methods
In: Han, Lifeng orcid:0000-0002-3221-2185 (2021) Meta-evaluation of machine translation evaluation methods. In: Workshop on Informetric and Scientometric Research (SIG-MET), 23-24 Oct 2021, Online. (2021)
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4
paracorp ...
Rajeg, Gede Primahadi Wijaya. - : Open Science Framework, 2021
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5
Learning Approximate and Exact Numeral Systems via Reinforcement Learning ...
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6
Mind the Gap: Language Data, Their Producers, and the Scientific Process (Crazy New Idea) ...
Weber, Tobias. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021
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7
Arabic Language and Computers. Application of Computational Linguistics to serve the Arabic Language
In: ALTRALANG Journal; Vol 3 No 01 (2021): ALTRALANG Journal Volume: 03 Issue: 01 / July 2021; 138-145 ; 2710-8619 ; 2710-7922 ; 10.52919/altralang.v3i01 (2021)
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8
Google Assistant's Interpreter Mode: is it really an interpreter?
Zouaoui, Safa. - : Université de Genève, 2021
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