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- W4309587059 abstract "Native language identification (NLI) is the task of automatically identifying the native language (L1) of an individual based on their language production in a learned language. It is useful for a variety of purposes including marketing, security and educational applications. NLI is usually framed as a multi-label classification task, where numerous designed features are combined to achieve state-of-the-art results. Recently deep generative approach based on transformer decoders (GPT-2) outperformed its counterparts and achieved the best results on the NLI benchmark datasets. We investigate this approach to determine the practical implications compared to traditional state-of-the-art NLI systems. We introduce transformer adapters to address memory limitations and improve training/inference speed to scale NLI applications for production." @default.
- W4309587059 created "2022-11-28" @default.
- W4309587059 creator A5054143133 @default.
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- W4309587059 date "2022-11-18" @default.
- W4309587059 modified "2023-09-27" @default.
- W4309587059 title "Scaling Native Language Identification with Transformer Adapters" @default.
- W4309587059 doi "https://doi.org/10.48550/arxiv.2211.10117" @default.
- W4309587059 hasPublicationYear "2022" @default.
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