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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 The effect of domain and diacritics in Yorùbá-English neural machine translation Adelani, David,; Ruiter, Dana; Alabi, Jesujoba,... In: 18th Biennial Machine Translation Summit ; https://hal.inria.fr/hal-03350967 ; 18th Biennial Machine Translation Summit, Aug 2021, Orlando, United States (2021) BASE Show details 2 The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation ... Adelani, David I.; Ruiter, Dana; Alabi, Jesujoba O.. - : arXiv, 2021 BASE Show details 3 Emoji-Based Transfer Learning for Sentiment Tasks ... Boy, Susann; Ruiter, Dana; Klakow, Dietrich. - : arXiv, 2021 BASE Show details 4 EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT ... Tchistiakova, Svetlana; Alabi, Jesujoba; Chowdhury, Koel Dutta. - : arXiv, 2021 BASE Show details 5 Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ... Ruiter, Dana; Klakow, Dietrich; van Genabith, Josef; España-Bonet, Cristina. - : arXiv, 2021 Abstract: For most language combinations, parallel data is either scarce or simply unavailable. To address this, unsupervised machine translation (UMT) exploits large amounts of monolingual data by using synthetic data generation techniques such as back-translation and noising, while self-supervised NMT (SSNMT) identifies parallel sentences in smaller comparable data and trains on them. To date, the inclusion of UMT data generation techniques in SSNMT has not been investigated. We show that including UMT techniques into SSNMT significantly outperforms SSNMT and UMT on all tested language pairs, with improvements of up to +4.3 BLEU, +50.8 BLEU, +51.5 over SSNMT, statistical UMT and hybrid UMT, respectively, on Afrikaans to English. We further show that the combination of multilingual denoising autoencoding, SSNMT with backtranslation and bilingual finetuning enables us to learn machine translation even for distant language pairs for which only small amounts of monolingual data are available, e.g. yielding BLEU scores ... : 11 pages, 8 figures, accepted at MT-Summit 2021 (Research Track) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2107.08772 https://arxiv.org/abs/2107.08772 BASE Hide details 6 Modeling Profanity and Hate Speech in Social Media with Semantic Subspaces ... 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; Hahn, Vanessa; Klakow, Dietrich. - : Underline Science Inc., 2021 BASE Show details

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