# Scaling Native Language Identification with Transformer Adapters

arXiv: https://arxiv.org/abs/2211.10117 · HTML: https://mlsanity.com/paper/2211.10117/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [AdapterDrop: On the Efficiency of Adapters in Transformers](https://mlsanity.com/paper/2010.11918/) (2020)
- [Automatic Lexical Simplification for Turkish](https://mlsanity.com/paper/2201.05878/) (2022)

## Built on by (most cited first, 2 of 2)

- [Native Language Identification with Large Language Models](https://mlsanity.com/paper/2312.07819/) (2023)
- [Turkish Native Language Identification V2](https://mlsanity.com/paper/2307.14850/) (2023)

## Abstract (arXiv)

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.