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Learning New Vocabulary Implicitly During Sleep Transfers With Cross-Modal Generalization Into Wakefulness
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In: ISSN: 1662-4548 ; EISSN: 1662-453X ; Frontiers in Neuroscience ; https://hal.sorbonne-universite.fr/hal-03640595 ; Frontiers in Neuroscience, Frontiers, 2022, 16, pp.801666. ⟨10.3389/fnins.2022.801666⟩ (2022)
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| 2 |
MAGIC DUST FOR CROSS-LINGUAL ADAPTATION OF MONOLINGUAL WAV2VEC-2.0
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In: ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03544515 ; ICASSP 2022, May 2022, Singapour, Singapore (2022)
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Cross-lingual few-shot hate speech and offensive language detection using meta learning
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In: ISSN: 2169-3536 ; EISSN: 2169-3536 ; IEEE Access ; https://hal.archives-ouvertes.fr/hal-03559484 ; IEEE Access, IEEE, 2022, 10, pp.14880-14896. ⟨10.1109/ACCESS.2022.3147588⟩ (2022)
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Abstract:
International audience ; Automatic detection of abusive online content such as hate speech, offensive language, threats, etc. has become prevalent in social media, with multiple efforts dedicated to detecting this phenomenon in English. However, detecting hatred and abuse in low-resource languages is a non-trivial challenge. The lack of sufficient labeled data in low-resource languages and inconsistent generalization ability of transformer-based multilingual pre-trained language models for typologically diverse languages make these models inefficient in some cases. We propose a meta learning-based approach to study the problem of few-shot hate speech and offensive language detection in low-resource languages that will allow hateful or offensive content to be predicted by only observing a few labeled data items in a specific target language. We investigate the feasibility of applying a meta learning approach in cross-lingual few-shot hate speech detection by leveraging two meta learning models based on optimization-based and metric-based (MAML and Proto-MAML) methods. To the best of our knowledge, this is the first effort of this kind. To evaluate the performance of our approach, we consider hate speech and offensive language detection as two separate tasks and make two diverse collections of different publicly available datasets comprising 15 datasets across 8 languages for hate speech and 6 datasets across 6 languages for offensive language. Our experiments show that meta learning-based models outperform transfer learning-based models in a majority of cases, and that Proto-MAML is the best performing model, as it can quickly generalize and adapt to new languages with only a few labeled data points (generally, 16 samples per class yields an effective performance) to identify hateful or offensive content.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]; [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]; Cross-lingual classification; Few-shot learning; Hate speech; Meta learning; Offensive language; Transfer learning; XLMRoBERTa
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URL: https://doi.org/10.1109/ACCESS.2022.3147588
https://hal.archives-ouvertes.fr/hal-03559484
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| 4 |
Improving Scene Text Recognition for Indian Languages with Transfer Learning and Font Diversity
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In: Journal of Imaging; Volume 8; Issue 4; Pages: 86 (2022)
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| 5 |
Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface
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In: Sensors; Volume 22; Issue 2; Pages: 535 (2022)
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| 6 |
Cross-Lingual Transfer Learning for Arabic Task-Oriented Dialogue Systems Using Multilingual Transformer Model mT5
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In: Mathematics; Volume 10; Issue 5; Pages: 746 (2022)
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| 7 |
Comparative Study of Multiclass Text Classification in Research Proposals Using Pretrained Language Models
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4522 (2022)
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| 8 |
Leveraging Frozen Pretrained Written Language Models for Neural Sign Language Translation
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In: Information; Volume 13; Issue 5; Pages: 220 (2022)
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| 9 |
Analyzing COVID-19 Medical Papers Using Artificial Intelligence: Insights for Researchers and Medical Professionals
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 4 (2022)
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| 10 |
The Effects of Event Depictions in Second Language Phrasal Vocabulary Learning
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| 11 |
The Effects of Event Depictions in Second Language Phrasal Vocabulary Learning ...
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| 12 |
StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence
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| 13 |
An Empirical Study of Factors Affecting Language-Independent Models
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| 14 |
Neural-based Knowledge Transfer in Natural Language Processing
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| 15 |
Chinese Idioms: Stepping Into L2 Student’s Shoes
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In: Acta Linguistica Asiatica, Vol 12, Iss 1 (2022) (2022)
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| 16 |
FaceTuneGAN: Face Autoencoder for Convolutional Expression Transfer Using Neural Generative Adversarial Networks
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In: https://hal.inria.fr/hal-03462778 ; 2021 (2021)
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| 17 |
Cross-lingual Representation Learning for Natural Language Processing
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| 18 |
Hate speech and offensive language detection using transfer learning approaches ; Détection du discours de haine et du langage offensant utilisant des approches de Transfer Learning
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In: https://tel.archives-ouvertes.fr/tel-03276023 ; Document and Text Processing. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAS007⟩ (2021)
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| 19 |
Fostering teacher language awareness in a primary English-language immersion school in France: supporting teachers on the road to engaging students’ bilingual competencies
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In: ISSN: 0965-8416 ; Language Awareness ; https://hal.univ-lorraine.fr/hal-03573322 ; In press (2021)
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| 20 |
Investigating data sharing in speech recognition for an underresourced language: the case of algerian dialect
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In: 7th International Conference on Natural Language Processing - NATP 2021 ; https://hal.archives-ouvertes.fr/hal-03137048 ; 7th International Conference on Natural Language Processing - NATP 2021, Mar 2021, Vienna, Austria (2021)
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