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Meta-Analysis of the Functional Neuroimaging Literature with Probabilistic Logic Programming
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In: https://hal.archives-ouvertes.fr/hal-03590714 ; 2022 (2022)
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Integrating a Phrase Structure Corpus Grammar and a Lexical-Semantic Network: the HOLINET Knowledge Graph
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In: Proceedings of LREC 2022 ; https://hal-amu.archives-ouvertes.fr/hal-03655636 ; Proceedings of LREC 2022, Jun 2022, Marseille, France (2022)
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Caveats of Measuring Semantic Change of Cognates and Borrowings using Multilingual Word Embeddings
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In: LChange'22 - 3rd International Workshop on Computational Approaches to Historical Language Change 2022 ; https://hal.inria.fr/hal-03635005 ; LChange'22 - 3rd International Workshop on Computational Approaches to Historical Language Change 2022, May 2022, Dublin, Ireland (2022)
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DeepL et Google Translate face à l'ambiguïté phraséologique
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In: https://hal.archives-ouvertes.fr/hal-03583995 ; 2022 (2022)
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An Overview of Indian Spoken Language Recognition from Machine Learning Perspective
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In: ISSN: 2375-4699 ; EISSN: 2375-4702 ; ACM Transactions on Asian and Low-Resource Language Information Processing ; https://hal.inria.fr/hal-03616853 ; ACM Transactions on Asian and Low-Resource Language Information Processing, ACM, In press, ⟨10.1145/3523179⟩ (2022)
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Morphology in the Corsican Language Database (BDLC) : assessment and perspectives ; La morphologie dans la Banque de Données Langue Corse : bilan et perspectives
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In: ISSN: 1638-9808 ; EISSN: 1765-3126 ; Corpus ; https://hal.archives-ouvertes.fr/hal-03591866 ; Corpus, Bases, Corpus, Langage - UMR 7320, 2022, Corpus et données en morpholgie, ⟨10.4000/corpus.7115⟩ ; https://journals.openedition.org/corpus/7115 (2022)
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VEREINDEUTIGUNG ZUR KLASSIFIZIERUNG LEXIKALISCHER OBJEKTE ; DISAMBIGUATION FOR THE CLASSIFICATION OF LEXICAL ITEMS ; DÉSAMBÏGUISATION POUR LA CLASSIFICATION DE LEXÈMES
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In: https://hal.archives-ouvertes.fr/hal-03598242 ; France, Patent n° : EP3937059A1. 2022 (2022)
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Assessing the impact of OCR noise on multilingual event detection over digitised documents
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In: ISSN: 1432-5012 ; EISSN: 1432-1300 ; International Journal on Digital Libraries ; https://hal.archives-ouvertes.fr/hal-03635985 ; International Journal on Digital Libraries, Springer Verlag, 2022, ⟨10.1007/s00799-022-00325-2⟩ (2022)
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| 9 |
Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0
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In: Proceedings of the International Workshop on Challenges & Perspectives in Creating Large Language Models 2022 (BigScience 2022) ; https://hal.inria.fr/hal-03639144 ; Proceedings of the International Workshop on Challenges & Perspectives in Creating Large Language Models 2022 (BigScience 2022), May 2022, Dublin, France (2022)
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| 10 |
Évaluation des propriétés multilingues d'un embedding contextualisé
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In: EGC 2022 - Conférence francophone sur l'Extraction et la Gestion des Connaissances ; https://hal.archives-ouvertes.fr/hal-03578480 ; EGC 2022 - Conférence francophone sur l'Extraction et la Gestion des Connaissances, Jan 2022, Blois, France (2022)
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| 11 |
Probing Multilingual Cognate Prediction Models
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In: Findings of the Association for Computational Linguistics: ACL 2022 ; https://hal.inria.fr/hal-03614691 ; Findings of the Association for Computational Linguistics: ACL 2022, May 2022, Dublin, Ireland (2022)
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| 12 |
Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents
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In: Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II ; https://hal.archives-ouvertes.fr/hal-03635971 ; Matthias Hagen; Suzan Verberne; Craig Macdonald; Christin Seifert; Krisztian Balog; Kjetil Nørvåg; Vinay Setty. Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II, 13186, Springer International Publishing, pp.347-354, 2022, Lecture Notes in Computer Science, 978-3-030-99738-0. ⟨10.1007/978-3-030-99739-7_44⟩ (2022)
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| 13 |
Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
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In: Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021) ; https://hal.inria.fr/hal-03527328 ; Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021), Jan 2022, punta cana, Dominican Republic ; https://aclanthology.org/2021.wnut-1.47/ (2022)
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| 14 |
Automatic Speech Recognition and Query By Example for Creole Languages Documentation
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In: Findings of the Association for Computational Linguistics: ACL 2022 ; https://hal.archives-ouvertes.fr/hal-03625303 ; Findings of the Association for Computational Linguistics: ACL 2022, May 2022, Dublin, Ireland (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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| 16 |
Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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| 17 |
Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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| 18 |
PROTECT: A Pipeline for Propaganda Detection and Classification
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In: CLiC-it 2021- Italian Conference on Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03417019 ; CLiC-it 2021- Italian Conference on Computational Linguistics, Jan 2022, Milan, Italy (2022)
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| 19 |
PROTECT - A Pipeline for Propaganda Detection and Classification
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In: Eighth Italian Conference on Computational Linguistics (CLIC-it 2021) ; https://hal.archives-ouvertes.fr/hal-03417019 ; Eighth Italian Conference on Computational Linguistics (CLIC-it 2021), Jan 2022, Milan, Italy (2022)
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| 20 |
Building infrastructure for annotating medieval, classical and pre-orthographic languages: the Pyrrha ecosystem
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In: Digital Humanities 2022 (DH2022) ; https://hal.archives-ouvertes.fr/hal-03606756 ; Digital Humanities 2022 (DH2022), Jul 2022, Tokyo, Japan ; https://dh2022.adho.org/ (2022)
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