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Multilingual Language Model Adaptive Fine-Tuning: A Study on African Languages ...
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Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation? ...
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MasakhaNER: Named entity recognition for African languages
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021)
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The effect of domain and diacritics in Yorùbá-English neural machine translation
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In: 18th Biennial Machine Translation Summit ; https://hal.inria.fr/hal-03350967 ; 18th Biennial Machine Translation Summit, Aug 2021, Orlando, United States (2021)
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Preventing author profiling through zero-shot multilingual back-translation
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In: 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP) ; https://hal.inria.fr/hal-03350906 ; 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2021, Punta Cana, Dominica (2021)
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On the effect of normalization layers on Differentially Private training of deep Neural networks
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In: https://hal.inria.fr/hal-03475600 ; 2021 (2021)
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The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation ...
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Preventing Author Profiling through Zero-Shot Multilingual Back-Translation ...
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Abstract:
Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e.g. their gender or ethnicity. Style transfer is an effective way of transforming texts in order to remove any information that enables author profiling. However, for a number of current state-of-the-art approaches the improved privacy is accompanied by an undesirable drop in the down-stream utility of the transformed data. In this paper, we propose a simple, zero-shot way to effectively lower the risk of author profiling through multilingual back-translation using off-the-shelf translation models. We compare our models with five representative text style transfer models on three datasets across different domains. Results from both an automatic and a human evaluation show that our approach achieves the best overall performance while requiring no training data. We are able to lower the adversarial prediction of gender and race by up to $22\%$ while retaining $95\%$ of the original utility on ... : Accepted to EMNLP 2021 (Main Conference), 9 pages ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2109.09133 https://arxiv.org/abs/2109.09133
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MasakhaNER: Named Entity Recognition for African Languages ...
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Preventing Author Profiling through Zero-Shot Multilingual Back-Translation ...
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Transfer learning and distant supervision for multilingual Transformer models: A study on African languages
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In: 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) ; https://hal.inria.fr/hal-03350901 ; 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2020, Punta Cana, Dominica (2020)
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Distant supervision and noisy label learning for low resource named entity recognition: A study on Hausa and Yorùbá
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In: ICLR Workshops (AfricaNLP & PML4DC 2020) ; https://hal.archives-ouvertes.fr/hal-03359111 ; ICLR Workshops (AfricaNLP & PML4DC 2020), Apr 2020, Addis Ababa, Ethiopia (2020)
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Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages ...
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Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yorùbá and Twi ...
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Demographic Inference and Representative Population Estimates from Multilingual Social Media Data ...
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