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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 – 6 of 6 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 Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ... Pylypenko, Daria; Amponsah-Kaakyire, Kwabena; Chowdhury, Koel Dutta. - : arXiv, 2021 BASE Show details 5 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; España-Bonet, Cristina; Pylypenko, Daria; van Genabith, Josef. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.676/ 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) ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing URL: https://underline.io/lecture/38089-comparing-feature-engineering-and-feature-learning-approaches-for-multilingual-translationese-classification https://dx.doi.org/10.48448/fj59-8z46 BASE Hide details 6 Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi ... Alabi, Jesujoba O.; Amponsah-Kaakyire, Kwabena; Adelani, David I.. - : arXiv, 2019 BASE Show details

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