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Deep interactive text prediction and quality estimation in translation interfaces
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In: Hokamp, Christopher M. (2018) Deep interactive text prediction and quality estimation in translation interfaces. PhD thesis, Dublin City University. (2018)
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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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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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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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CNGL: Grading student answers by acts of translation
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In: Bicici, Ergun orcid:0000-0002-2293-2031 and van Genabith, Josef orcid:0000-0003-1322-7944 (2013) CNGL: Grading student answers by acts of translation. In: SEMEVAL, 14-15 Jun 2013, Atlanta, Georgia. (2013)
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CNGL-CORE: Referential translation machines for measuring semantic similarity
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In: Bicici, Ergun orcid:0000-0002-2293-2031 and van Genabith, Josef orcid:0000-0003-1322-7944 (2013) CNGL-CORE: Referential translation machines for measuring semantic similarity. In: *SEM, 13-14 Jun 2013, Atlanta, Georgia. (2013)
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Detecting grammatical errors with treebank-induced, probabilistic parsers
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In: Wagner, Joachim orcid:0000-0002-8290-3849 (2012) Detecting grammatical errors with treebank-induced, probabilistic parsers. PhD thesis, Dublin City University. (2012)
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Identifying high-impact sub-structures for convolution kernels in document-level sentiment classification
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In: Tu, Zhaopeng, He, Yifan, Foster, Jennifer orcid:0000-0002-7789-4853 , van Genabith, Josef orcid:0000-0003-1322-7944 , Liu, Qun and Shouxun, Lin (2012) Identifying high-impact sub-structures for convolution kernels in document-level sentiment classification. In: Annual Meeting of the Association for Computational Linguistics (ACL 2012), 9-11 Jul 2012, Jelu, Korea. (2012)
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Judging grammaticality: experiments in sentence classification
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In: Wagner, Joachim orcid:0000-0002-8290-3849 , Foster, Jennifer orcid:0000-0002-7789-4853 and van Genabith, Josef orcid:0000-0003-1322-7944 (2009) Judging grammaticality: experiments in sentence classification. CALICO Journal, 26 (3). pp. 474-490. ISSN 0742-7778 (2009)
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Towards a machine-learning architecture for lexical functional grammar parsing
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In: Chrupała, Grzegorz (2008) Towards a machine-learning architecture for lexical functional grammar parsing. PhD thesis, Dublin City University. (2008)
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Using machine-learning to assign function labels to parser output for Spanish
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In: Chrupała, Grzegorz and van Genabith, Josef (2006) Using machine-learning to assign function labels to parser output for Spanish. In: COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, 17-21 July 2006, Sydney, Australia. (2006)
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