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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. - : Underline Science Inc., 2021 BASE Show 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.; España-Bonet, Cristina. - : arXiv, 2019 Abstract: The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yorùbá and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and ... : 9 pages, 4 tables. Accepted at LREC 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1912.02481 https://dx.doi.org/10.48550/arxiv.1912.02481 BASE Hide details

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