Scaling Native Language Identification with Transformer Adapters

Ahmet Yavuz Uluslu; Gerold Schneider

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arXiv · 2211.10117

Scaling Native Language Identification with Transformer Adapters

Ahmet Yavuz Uluslu·Gerold Schneider

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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.

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BibTeX RIS

Ahmet Yavuz Uluslu, Gerold Schneider. 2022-11-18. Scaling Native Language Identification with Transformer Adapters. https://arxiv.org/abs/2211.10117

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