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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. - : arXiv, 2021 BASE Show 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; Kleinbauer, Thomas; Ruiter, Dana. - : Underline Science Inc., 2021 Abstract: Hate speech and profanity detection sufferfrom data sparsity, especially for languages other than English, due to the subjective nature of the tasks and the resulting annotation incompatibility of existing corpora. In this study, we identify profane subspaces in wordand sentence representations and explore their generalization capability on a variety of similar and distant target tasks in a zero-shot setting. This is done monolingually (German) and cross-lingually to closely-related (English), distantly-related (French) and non-related (Arabic) tasks. We observe that, on both similar and distant target tasks and across all languages, the subspace-based representations transfer more effectively than standard BERT representations in the zero-shot setting, with improvements between F1 +10.9 and F1 +42.9 over the baselines across all tested monolingual and cross-lingual scenarios. ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/c43q-de94 https://underline.io/lecture/30159-modeling-profanity-and-hate-speech-in-social-media-with-semantic-subspaces BASE Hide details

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