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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 Europarl Direct Translationese Dataset ... Amponsah-Kaakyire, Kwabena; Pylypenko, Daria; España-Bonet, Cristina. - : Zenodo, 2021 BASE Show details 2 Europarl Direct Translationese Dataset ... Amponsah-Kaakyire, Kwabena; Pylypenko, Daria; España-Bonet, Cristina. - : Zenodo, 2021 BASE Show details 3 Europarl Direct Translationese Dataset ... Amponsah-Kaakyire, Kwabena; Pylypenko, Daria; España-Bonet, Cristina. - : Zenodo, 2021 BASE Show details 4 Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ... Ruiter, Dana; Klakow, Dietrich; van Genabith, Josef. - : arXiv, 2021 BASE Show details 5 Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ... Pylypenko, Daria; Amponsah-Kaakyire, Kwabena; Chowdhury, Koel Dutta. - : arXiv, 2021 BASE Show details 6 Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Amponsah-Kaakyire, Kwabena; Dutta Chowdhury, Koel. - : Underline Science Inc., 2021 BASE Show details 7 Automatic classification of human translation and machine translation : a study from the perspective of lexical diversity Fu, Yingxue; Nederhof, Mark Jan. - : Linkoping University Electronic Press, 2021 Abstract: By using a trigram model and fine-tuning a pretrained BERT model for sequence classification, we show that machine translation and human translation can be classified with an accuracy above chance level, which suggests that machine translation and human translation are different in a systematic way. The classification accuracy of machine translation is much higher than of human translation. We show that this may be explained by the difference in lexical diversity between machine translation and human translation. If machine translation has independent patterns from human translation, automatic metrics which measure the deviation of machine translation from human translation may conflate difference with quality. Our experiment with two different types of automatic metrics shows correlation with the result of the classification task. Therefore, we suggest the difference in lexical diversity between machine translation and human translation be given more attention in machine translation evaluation. ; Publisher PDF Keyword: 3rd-DAS; Artificial Intelligence; Q Science (General); Q1 URL: https://aclanthology.org/previews/ingest-nodalida/2021.motra-1.10/ http://hdl.handle.net/10023/23304 BASE Hide details 8 Query Translation for Cross-lingual Search in the Academic Search Engine PubPsych ... España-Bonet, Cristina; Stiller, Juliane; Ramthun, Roland. - : PsychArchives, 2018 BASE Show details 9 Query Translation for Cross-lingual Search in the Academic Search Engine PubPsych ... España-Bonet, Cristina; Stiller, Juliane; Ramthun, Roland. - : PsychArchives, 2018 BASE Show details 10 An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification ... España-Bonet, Cristina; Varga, Ádám Csaba; Barrón-Cedeño, Alberto. - : arXiv, 2017 BASE Show details

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