Native-Language Identification with Attention

Stian Steinbakken, Björn Gambäck

Abstract: The paper explores how an attention-based approach can increase performance on the task of native-language identification (NLI), i.e., to identify an author’s first language given information expressed in a second language. Previously, Support Vector Machines have consistently outperformed deep learning-based methods on the TOEFL11 data set, the de facto standard for evaluating NLI systems. The attention-based system BERT (Bidirectional Encoder Representations from Transformers) was first tested in isolation on the TOEFL11 data set, then used in a meta-classifier stack in combination with traditional techniques to produce an accuracy of 0.853. However, more labelled NLI data is now available, so BERT was also trained on the much larger Reddit-L2 data set, containing 50 times as many examples as previously used for English NLI, giving an accuracy of 0.902 on the Reddit-L2 in-domain test scenario, improving the state-of-the-art by 21.2 percentage points.