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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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Attributions of Successful English Language Learners in Transfer-Level English
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In: Doctoral Dissertations and Projects (2022)
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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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Abstract:
Due to the promising performance of pre-trained language models for task-oriented dialogue systems (DS) in English, some efforts to provide multilingual models for task-oriented DS in low-resource languages have emerged. These efforts still face a long-standing challenge due to the lack of high-quality data for these languages, especially Arabic. To circumvent the cost and time-intensive data collection and annotation, cross-lingual transfer learning can be used when few training data are available in the low-resource target language. Therefore, this study aims to explore the effectiveness of cross-lingual transfer learning in building an end-to-end Arabic task-oriented DS using the mT5 transformer model. We use the Arabic task-oriented dialogue dataset (Arabic-TOD) in the training and testing of the model. We present the cross-lingual transfer learning deployed with three different approaches: mSeq2Seq, Cross-lingual Pre-training (CPT), and Mixed-Language Pre-training (MLT). We obtain good results for our model compared to the literature for Chinese language using the same settings. Furthermore, cross-lingual transfer learning deployed with the MLT approach outperform the other two approaches. Finally, we show that our results can be improved by increasing the training dataset size.
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Keyword:
Arabic language; cross-lingual transfer learning; mixed-language pre-training; mT5; multilingual transformer model; natural language processing; task-oriented dialogue systems
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URL: https://doi.org/10.3390/math10050746
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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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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 |
The Effects of Event Depictions in Second Language Phrasal Vocabulary Learning
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| 10 |
ETHNOCULTURAL AND SOCIOLINGUISTIC FACTORS IN TEACHING RUSSIAN AS A FOREIGN LANGUAGE ...
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| 11 |
The Effects of Event Depictions in Second Language Phrasal Vocabulary Learning ...
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StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence
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An Empirical Study of Factors Affecting Language-Independent Models
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| 16 |
„A Hund is er scho’“. Die Migration eines Ausdrucks und seine bayerisch-ungarische Transfergeschichte
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Neural-based Knowledge Transfer in Natural Language Processing
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| 18 |
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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| 19 |
Some remarks on the history of transfer in language studies
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In: Proceedings of the Linguistic Society of America; Vol 7, No 1 (2022): Proceedings of the Linguistic Society of America; 5206 ; 2473-8689 (2022)
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| 20 |
The Value and Use of the Telugu Language in Young Adults of Telugu-Speaking Backgrounds in New Zealand
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