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INFODENS: An Open-source Framework for Learning Text Representations ...
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Query Translation for Cross-lingual Search in the Academic Search Engine PubPsych ...
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Query Translation for Cross-lingual Search in the Academic Search Engine PubPsych ...
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A Hybrid Machine Translation Framework for an Improved Translation Workflow
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Pal, Santanu. - : Saarländische Universitäts- und Landesbibliothek, 2018
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Massively Multilingual Neural Grapheme-to-Phoneme Conversion ...
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An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification ...
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32 |
Predicting the Law Area and Decisions of French Supreme Court Cases ...
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33 |
Pluricentric languages : automatic identification and linguistic variation ; Plurizentrische Sprachen : automatische Spracherkennung und linguistische Variation
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34 |
Improving translation memory matching and retrieval using paraphrases
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In: 30 ; 1 ; 19 ; 40 (2016)
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A Minimally Supervised Approach for Synonym Extraction with Word Embeddings
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In: Prague Bulletin of Mathematical Linguistics , Vol 105, Iss 1, Pp 111-142 (2016) (2016)
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Statistical post-editing and quality estimation for machine translation systems
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In: Béchara, Hanna (2014) Statistical post-editing and quality estimation for machine translation systems. Master of Science thesis, Dublin City University. (2014)
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Abstract:
Statistical post-editing (SPE) has been successfully applied to RBMT systems and, to a less successful extent, to some SMT systems. This thesis investigates the impact of SPE on SMT systems. We apply SPE to an SMT system using a new context-modelling approach to preserve some aspects of source information in the second stage translation. This technique yields mixed results, but fails to consistently improve the output over the baseline. Furthermore, we compared the results to those of an RBMT+SPE system and a pure SMT system, using both automatic and human evaluation methods. Results show that while automatic evaluation metrics favour a pure SMT system, manual evaluators prefer the output provided by the combined RBMT+SPE system. We investigate the use machine learning methods to predict which sentences would benefit from post-editing, however, as the oracle score for both SMT and SMT+SPE was not much higher than the two systems alone, we decided to compare two systems that had a higher upper bound. Combining our analysis with machine learning techniques for quality estimation, we are able to improve the overall output by automatically selecting the best sentences from each of the SMT and RBMT+SPE systems.
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Keyword:
Computational linguistics; Machine translating; Post-editing; Quality Estimation
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URL: http://doras.dcu.ie/19751/
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Predicting sentence translation quality using extrinsic and language independent features
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In: Bicici, Ergun, Groves, Declan and van Genabith, Josef orcid:0000-0003-1322-7944 (2013) Predicting sentence translation quality using extrinsic and language independent features. Machine Translation, 27 (3-4). pp. 171-192. ISSN 0922-6567 (2013)
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Working with a small dataset - semi-supervised dependency parsing for Irish
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In: Lynn, Teresa, Foster, Jennifer orcid:0000-0002-7789-4853 , Dras, Mark orcid:0000-0001-9908-7182 and van Genabith, Josef orcid:0000-0003-1322-7944 (2013) Working with a small dataset - semi-supervised dependency parsing for Irish. In: Fourth Workshop on Statistical Parsing of Morphologically Rich Languages, 18 Oct 2013, Seattle, WA. USA. (2013)
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Computer assisted (language) learning (CA(L)L) for the inclusive classroom
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Greene, Cara N.. - : Dublin City University. Centre for Next Generation Localisation (CNGL), 2013. : Dublin City University. National Centre for Language Technology (NCLT), 2013. : Dublin City University. School of Computing, 2013
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In: Greene, Cara N. (2013) Computer assisted (language) learning (CA(L)L) for the inclusive classroom. PhD thesis, Dublin City University. (2013)
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Domain adaptation for statistical machine translation of corporate and user-generated content
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In: Banerjee, Pratyush (2013) Domain adaptation for statistical machine translation of corporate and user-generated content. PhD thesis, Dublin City University. (2013)
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