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Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output ...
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Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation ...
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Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
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Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation ...
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A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment ...
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Automatic classification of human translation and machine translation : a study from the perspective of lexical diversity
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
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In: Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02892154 ; Language Resources and Evaluation Conference, ELDA/ELRA, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/en/ (2020)
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
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Language service provision in the 21st century: challenges, opportunities and educational perspectives for translation studies
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In: ISBN: 9788869234934 ; Bologna Process beyond 2020: Fundamental values of the EHEA pp. 297-303 (2020)
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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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An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification ...
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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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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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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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Abstract:
We invent referential translation machines (RTMs), a computational model for identifying the translation acts between any two data sets with respect to a reference corpus selected in the same domain, which can be used for automatically grading student answers. RTMs make quality and semantic similarity judgments possible by using retrieved relevant training data as interpretants for reaching shared semantics. An MTPP (machine translation performance predictor) model derives features measuring the closeness of the test sentences to the training data, the difficulty of translating them, and the presence of acts of translation involved. We view question answering as translation from the question to the answer, from the question to the reference answer, from the answer to the reference answer, or from the question and the answer to the reference answer. Each view is modeled by an RTM model, giving us a new perspective on the ternary relationship between the question, the answer, and the reference answer. We show that all RTM models contribute and a prediction model based on all four perspectives performs the best. Our prediction model is the $2$nd best system on some tasks according to the official results of the Student Response Analysis (SRA 2013) challenge.
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Keyword:
Artificial intelligence; Computational linguistics; computational semantics; Information retrieval; Machine learning; Machine translating; machine translation
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URL: http://doras.dcu.ie/18563/
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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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Working with a small dataset - semi-supervised dependency parsing for Irish
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