2019
EMNLP
EMNLP 2019
Dialect Text Normalization to Normative Standard Finnish
Abstract
AbstractWe compare different LSTMs and transformer models in terms of their effectiveness in normalizing dialectal Finnish into the normative standard Finnish. As dialect is the common way of communication for people online in Finnish, such a normalization is a necessary step to improve the accuracy of the existing Finnish NLP tools that are tailored for normative Finnish text. We work on a corpus consisting of dialectal data of 23 distinct Finnish dialects. The best functioning BRNN approach lowers the initial word error rate of the corpus from 52.89 to 5.73.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Natural Language Processing
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Keyword Pioneer
— dialect text normalization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Deep Learning > Architectures > Transformers
Deep Learning > Architectures > Neural Networks
Natural Language Processing > Generation > Text Generation
Natural Language Processing > Applications > Text Generation
Artificial Intelligence > Core AI > Natural Language Processing
Natural Language Processing > Applications > Text Processing
Deep Learning > Learning Types > Sequence Modeling