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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 – 12 of 12 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; van Genabith, Josef; España-Bonet, Cristina. - : arXiv, 2021 Abstract: Traditional hand-crafted linguistically-informed features have often been used for distinguishing between translated and original non-translated texts. By contrast, to date, neural architectures without manual feature engineering have been less explored for this task. In this work, we (i) compare the traditional feature-engineering-based approach to the feature-learning-based one and (ii) analyse the neural architectures in order to investigate how well the hand-crafted features explain the variance in the neural models' predictions. We use pre-trained neural word embeddings, as well as several end-to-end neural architectures in both monolingual and multilingual settings and compare them to feature-engineering-based SVM classifiers. We show that (i) neural architectures outperform other approaches by more than 20 accuracy points, with the BERT-based model performing the best in both the monolingual and multilingual settings; (ii) while many individual hand-crafted translationese features correlate with ... : 9 pages, 5 pages appendix, 2 figures, 7 tables. The first 3 authors contributed equally. Accepted to EMNLP 2021, Main Conference ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2109.07604 https://dx.doi.org/10.48550/arxiv.2109.07604 BASE Hide details 6 Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Herbig, Nico; Krueger, Antonio. - : Underline Science Inc., 2021 BASE Show details 7 Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Liu, Qiuhui; van Genabith, Josef. - : Underline Science Inc., 2021 BASE Show details 8 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 9 Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Liu, Qiuhui; van Genabith, Josef. - : Underline Science Inc., 2021 BASE Show details 10 A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; van Genabith, Josef; Zhang, Jingyi. - : Underline Science Inc., 2021 BASE Show details 11 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 BASE Show details 12 Transformer-based NMT : modeling, training and implementation Xu, Hongfei. - : Saarländische Universitäts- und Landesbibliothek, 2021 BASE Show details

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