A Shallow Neural Network for Native Language Identification with Character N-grams
This paper describes the systems submitted by GadjahMada team to the Native Language Identification (NLI) Shared Task 2017. Our models used a continuous representation of character n-grams which are learned jointly with feed-forward neural network classifier. Character n-grams have been proved to be effective for style-based identification tasks including NLI. Results on the test set demonstrate that the proposed model performs very well on essay and fusion tracks by obtaining more than 0.8 on both F-macro score and accuracy.
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