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- W4328120207 abstract "NLP technologies are uneven for the world's languages as the state-of-the-art models are only available for a handful of them. This is because developing such a language-specific model needs rich monolingual resources and labelled datasets which are partly or completely missing for many languages that are low-resource.This inequality in multilingual resources and limited capabilities of existing NLP models, especially for low-resource languages, drive us to explore more sophisticated solutions. This thesis presents a unified approach that consists of a set of novel methods within the context of multilingual learning and adaptation to move current NLP technologies beyond a small-set of resource-rich languages. We evaluate these techniques by using particular tasks and targeted use cases such as zero-shot or unsupervised learning scenarios. We believe that our findings can be a base for further analysis and our techniques can be extended to the billion-scale language models. While neural language models become progressively larger in size, more effective and efficient adaptation methods can enable NLP technologies to be more fair and inclusive." @default.
- W4328120207 created "2023-03-22" @default.
- W4328120207 creator A5063061204 @default.
- W4328120207 date "2023-03-21" @default.
- W4328120207 modified "2023-09-30" @default.
- W4328120207 title "Multilingual Learning and Adaptation for Neural Language Models" @default.
- W4328120207 doi "https://doi.org/10.33612/diss.602546936" @default.
- W4328120207 hasPublicationYear "2023" @default.
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