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INFODENS: An Open-source Framework for Learning Text Representations ...
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| 22 |
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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| 30 |
Massively Multilingual Neural Grapheme-to-Phoneme Conversion ...
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| 31 |
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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| 36 |
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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| 37 |
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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Abstract:
We develop a top performing model for automatic, accurate, and language independent prediction of sentence-level statistical machine translation (SMT) quality with or without looking at the translation outputs. We derive various feature functions measuring the closeness of a given test sentence to the training data and the difficulty of translating the sentence. We describe \texttt{mono} feature functions that are based on statistics of only one side of the parallel training corpora and \texttt{duo} feature functions that incorporate statistics involving both source and target sides of the training data. Overall, we describe novel, language independent, and SMT system extrinsic features for predicting the SMT performance, which also rank high during feature ranking evaluations. We experiment with different learning settings, with or without looking at the translations, which help differentiate the contribution of different feature sets. We apply partial least squares and feature subset selection, both of which improve the results and we present ranking of the top features selected for each learning setting, providing an exhaustive analysis of the extrinsic features used. We show that by just looking at the test source sentences and not using the translation outputs at all, we can achieve better performance than a baseline system using SMT model dependent features that generated the translations. Furthermore, our prediction system is able to achieve the $2$nd best performance overall according to the official results of the Quality Estimation Task (QET) challenge when also looking at the translation outputs. Our representation and features achieve the top performance in QET among the models using the SVR learning model.
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Keyword:
Computational linguistics; Machine learning; Machine translating; Performance prediction; Quality estimation; Statistical machine translation
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URL: http://doras.dcu.ie/19283/
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| 38 |
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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| 39 |
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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