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Multilingual Language Model Adaptive Fine-Tuning: A Study on African Languages ...
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Preventing author profiling through zero-shot multilingual back-translation
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)
Abstract: International audience ; 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-theart approaches the improved privacy is accompanied by an undesirable drop in the downstream 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 downstream tasks.
Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
URL: https://hal.inria.fr/hal-03350906/document
https://hal.inria.fr/hal-03350906
https://hal.inria.fr/hal-03350906/file/adelani_EMNLP2021.pdf
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3
On the effect of normalization layers on Differentially Private training of deep Neural networks
In: https://hal.inria.fr/hal-03475600 ; 2021 (2021)
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Adapting Language Models When Training on Privacy-Transformed Data
In: INTERSPEECH 2021 ; https://hal.inria.fr/hal-03189354 ; 2021 (2021)
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Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study ...
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6
ANEA: Distant Supervision for Low-Resource Named Entity Recognition ...
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7
Emoji-Based Transfer Learning for Sentiment Tasks ...
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8
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ...
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9
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation ...
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10
Modeling Profanity and Hate Speech in Social Media with Semantic Subspaces ...
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11
Exploring the Potential of Lexical Paraphrases for Mitigating Noise-Induced Comprehension Errors ...
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12
On the Correlation of Context-Aware Language Models With the Intelligibility of Polish Target Words to Czech Readers
In: Front Psychol (2021)
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13
Transfer learning and distant supervision for multilingual Transformer models: A study on African languages
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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14
Distant supervision and noisy label learning for low resource named entity recognition: A study on Hausa and Yorùbá
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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15
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages ...
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16
Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification ...
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17
On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers ...
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18
A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English ...
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A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English ...
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20
The Role of Predicates in Opinion Holder Extraction
Wiegand, Michael [Verfasser]; Klakow, Dietrich [Verfasser]. - Mannheim : Institut für Deutsche Sprache, Bibliothek, 2019
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