@inproceedings{steinbakken-gamback-2020-native,
title = "Native-Language Identification with Attention",
author = {Steinbakken, Stian and
Gamb{\"a}ck, Bj{\"o}rn},
editor = "Bhattacharyya, Pushpak and
Sharma, Dipti Misra and
Sangal, Rajeev",
booktitle = "Proceedings of the 17th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2020",
address = "Indian Institute of Technology Patna, Patna, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2020.icon-main.35",
pages = "261--271",
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.",
}
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%0 Conference Proceedings
%T Native-Language Identification with Attention
%A Steinbakken, Stian
%A Gambäck, Björn
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F steinbakken-gamback-2020-native
%X 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.
%U https://aclanthology.org/2020.icon-main.35
%P 261-271
Markdown (Informal)
[Native-Language Identification with Attention](https://aclanthology.org/2020.icon-main.35) (Steinbakken & Gambäck, ICON 2020)
ACL
- Stian Steinbakken and Björn Gambäck. 2020. Native-Language Identification with Attention. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 261–271, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).