Unsupervised Data Validation Methods for Efficient Model Training
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913540187619328 |
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| author | Paniv, Yurii |
| author_facet | Paniv, Yurii |
| contents | This paper investigates the challenges and potential solutions for improving machine learning systems for low-resource languages. State-of-the-art models in natural language processing (NLP), text-to-speech (TTS), speech-to-text (STT), and vision-language models (VLM) rely heavily on large datasets, which are often unavailable for low-resource languages. This research explores key areas such as defining "quality data," developing methods for generating appropriate data and enhancing accessibility to model training. A comprehensive review of current methodologies, including data augmentation, multilingual transfer learning, synthetic data generation, and data selection techniques, highlights both advancements and limitations. Several open research questions are identified, providing a framework for future studies aimed at optimizing data utilization, reducing the required data quantity, and maintaining high-quality model performance. By addressing these challenges, the paper aims to make advanced machine learning models more accessible for low-resource languages, enhancing their utility and impact across various sectors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07880 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unsupervised Data Validation Methods for Efficient Model Training Paniv, Yurii Computation and Language Machine Learning This paper investigates the challenges and potential solutions for improving machine learning systems for low-resource languages. State-of-the-art models in natural language processing (NLP), text-to-speech (TTS), speech-to-text (STT), and vision-language models (VLM) rely heavily on large datasets, which are often unavailable for low-resource languages. This research explores key areas such as defining "quality data," developing methods for generating appropriate data and enhancing accessibility to model training. A comprehensive review of current methodologies, including data augmentation, multilingual transfer learning, synthetic data generation, and data selection techniques, highlights both advancements and limitations. Several open research questions are identified, providing a framework for future studies aimed at optimizing data utilization, reducing the required data quantity, and maintaining high-quality model performance. By addressing these challenges, the paper aims to make advanced machine learning models more accessible for low-resource languages, enhancing their utility and impact across various sectors. |
| title | Unsupervised Data Validation Methods for Efficient Model Training |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.07880 |