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    • Adelani, David Ifeoluwa (4)
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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 – 4 of 4 1 Multilingual Language Model Adaptive Fine-Tuning: A Study on African Languages ... Alabi, Jesujoba O.; Adelani, David Ifeoluwa; Mosbach, Marius. - : arXiv, 2022 BASE Show details 2 On the effect of normalization layers on Differentially Private training of deep Neural networks Davody, Ali; Adelani, David Ifeoluwa; Kleinbauer, Thomas... In: https://hal.inria.fr/hal-03475600 ; 2021 (2021) BASE Show details 3 Preventing Author Profiling through Zero-Shot Multilingual Back-Translation ... Adelani, David Ifeoluwa; Zhang, Miaoran; Shen, Xiaoyu. - : arXiv, 2021 BASE Show details 4 Distant supervision and noisy label learning for low resource named entity recognition: A study on Hausa and Yorùbá Adelani, David Ifeoluwa; Hedderich, Michael,; Zhu, Dawei; Van Den Berg, Esther; Klakow, Dietrich In: ICLR Workshops (AfricaNLP & PML4DC 2020) ; https://hal.archives-ouvertes.fr/hal-03359111 ; ICLR Workshops (AfricaNLP & PML4DC 2020), Apr 2020, Addis Ababa, Ethiopia (2020) Abstract: International audience ; The lack of labeled training data has limited the development of natural language processing tools, such as named entity recognition, for many languages spoken in developing countries. Techniques such as distant and weak supervision can be used to create labeled data in a (semi-) automatic way. Additionally, to alleviate some of the negative effects of the errors in automatic annotation, noise-handling methods can be integrated. Pretrained word embeddings are another key component of most neural named entity classifiers. With the advent of more complex contextual word embeddings, an interesting trade-off between model size and performance arises. While these techniques have been shown to work well in highresource settings, we want to study how they perform in low-resource scenarios. In this work, we perform named entity recognition for Hausa and Yorùbá, two languages that are widely spoken in several developing countries. We evaluate different embedding approaches and show that distant supervision can be successfully leveraged in a realistic low-resource scenario where it can more than double a classifier's performance. Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL] URL: https://hal.archives-ouvertes.fr/hal-03359111/file/adelani_AfricaNLP2020.pdf https://hal.archives-ouvertes.fr/hal-03359111/document https://hal.archives-ouvertes.fr/hal-03359111 BASE Hide details

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