Neural Networks and Spelling Features for Native Language Identification

Abstract

We present the RUG-SU team's submission at the Native Language Identification Shared Task 2017. We combine several approaches into an ensemble, based on spelling error features, a simple neural network using word representations, a deep residual network using word and character features, and a system based on a recurrent neural network. Our best system is an ensemble of neural networks, reaching an F1 score of 0.8323. Although our system is not the highest ranking one, we do outperform the baseline by far.

Multitask deep learning for native language identification

“…In one paper [13], the authors evaluated several architectures while considering several different versions of the N-gram features. They achieved accuracy of 0.83 using an ensemble residual neural network with word uni-grams and character 5-grams and 6-grams as input features.…”

“…This was the most accurate model in the competition. Authors of [13] also outlined the different options for features and architectures that can be utilized. Bjerva et al [13] discussed PoS tagged sentences, continuous bag of words features, and spelling features.…”

“…Authors of [13] also outlined the different options for features and architectures that can be utilized. Bjerva et al [13] discussed PoS tagged sentences, continuous bag of words features, and spelling features. One important note to take from [13] is that the performance of any model is lower when using external sources, such as those features mentioned above.…”

“…Bjerva et al [13] discussed PoS tagged sentences, continuous bag of words features, and spelling features. One important note to take from [13] is that the performance of any model is lower when using external sources, such as those features mentioned above. Another takeaway is that DNNs are able to perform this classification, although traditional methods such as SVMs still appear to be better.…”