A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017

Yoo Rhee Oh, Hyung-Bae Jeon, Hwa Jeon Song, Yun-Kyung Lee, Jeon-Gue Park, Yun-Keun Lee


Abstract
This paper proposes a deep-learning based native-language identification (NLI) using a latent semantic analysis (LSA) as a participant (ETRI-SLP) of the NLI Shared Task 2017 where the NLI Shared Task 2017 aims to detect the native language of an essay or speech response of a standardized assessment of English proficiency for academic purposes. To this end, we use the six unit forms of a text data such as character 4/5/6-grams and word 1/2/3-grams. For each unit form of text data, we convert it into a count-based vector, extract a 2000-rank LSA feature, and perform a linear discriminant analysis (LDA) based dimension reduction. From the count-based vector or the LSA-LDA feature, we also obtain the output prediction values of a support vector machine (SVM) based classifier, the output prediction values of a deep neural network (DNN) based classifier, and the bottleneck values of a DNN based classifier. In order to incorporate the various kinds of text-based features and a speech-based i-vector feature, we design two DNN based ensemble classifiers for late fusion and early fusion, respectively. From the NLI experiments, the F1 (macro) scores are obtained as 0.8601, 0.8664, and 0.9220 for the essay track, the speech track, and the fusion track, respectively. The proposed method has comparable performance to the top-ranked teams for the speech and fusion tracks, although it has slightly lower performance for the essay track.
Anthology ID:
W17-5047
Volume:
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
413–422
Language:
URL:
https://aclanthology.org/W17-5047
DOI:
10.18653/v1/W17-5047
Bibkey:
Cite (ACL):
Yoo Rhee Oh, Hyung-Bae Jeon, Hwa Jeon Song, Yun-Kyung Lee, Jeon-Gue Park, and Yun-Keun Lee. 2017. A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 413–422, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017 (Oh et al., BEA 2017)
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PDF:
https://aclanthology.org/W17-5047.pdf