NLPRL@INLI-2018: Hybrid gated LSTM-CNN model for Indian native language identification - Mundotiya R.K.; Singh M.; Singh A.K. (My University, 2025)
Native language identification (NLI) focuses on determining the native language of the author based on the writing style in English. Indian native language identification is a challenging task based on users comments and posts on social media. To solve this problem, we present a hybrid gated LSTM-CNN model to solve this problem. The final vector of a sentence is generated at hybrid gate by joining the two distinct vector of a sentence. Gate seeks the optimum mixture of the LSTM and CNN level outputs. The input word for LSTM and CNN are projected into high-dimensional space by embedding technique. We obtained 88.50% accuracy during training on the provided social media dataset, and 17.10% is reported in the final testing done by Indian native language identification (INLI) workshop organizers. © 2018 CEUR-WS. All Rights Reserved.