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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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22 |
Measuring Terminology Consistency in Translated Corpora: Implementation of the Herfindahl-Hirshman Index
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In: Information; Volume 13; Issue 2; Pages: 43 (2022)
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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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24 |
The Role of Task Complexity and Dominant Articulatory Routines in the Acquisition of L3 Spanish
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In: Languages; Volume 7; Issue 2; Pages: 90 (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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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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27 |
The Effects of Event Depictions in Second Language Phrasal Vocabulary Learning
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30 |
ETHNOCULTURAL AND SOCIOLINGUISTIC FACTORS IN TEACHING RUSSIAN AS A FOREIGN LANGUAGE ...
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The Effects of Event Depictions in Second Language Phrasal Vocabulary Learning ...
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34 |
Toward an Epistemic Web
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In: 197 ; RatSWD Working Paper Series ; 22 (2022)
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35 |
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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Abstract:
Scaling existing applications and solutions to multiple human languages has traditionally proven to be difficult, mainly due to the language-dependent nature of preprocessing and feature engineering techniques employed in traditional approaches. In this work, we empirically investigate the factors affecting language-independent models built with multilingual representations, including task type, language set and data resource. On two most representative Natural Language Processing tasks --- sentence classification and sequence labeling, we show that language-independent models can be comparable to or even outperforms the models trained using monolingual data, and they are generally more effective on sentence classification. We experiment language-independent models with many different languages and show that they are more suitable for typologically similar languages. We also explore the effects of different data sizes when training and testing language-independent models, and demonstrate that they are not only suitable for high-resource languages, but also very effective in low-resource languages.
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Keyword:
Analytics Technologies for Industry Platforms; Business Intelligence; Case studies of Artificial Intelligence; case study; multilingual ai; natural language processing; transfer learning
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URL: https://doi.org/10.24251/HICSS.2022.149 http://hdl.handle.net/10125/79481
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38 |
„A Hund is er scho’“. Die Migration eines Ausdrucks und seine bayerisch-ungarische Transfergeschichte
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39 |
Neural-based Knowledge Transfer in Natural Language Processing
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40 |
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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