AI

Multilingual Code-switching Identification via LSTM Recurrent Neural Networks

Abstract

This paper describes the HHU-UH-G system submitted to the EMNLP 2016 Second Workshop on Computational Approaches to Code Switching. Our system ranked first place for Arabic (MSA-Egyptian) with an F1-score of 0.83 and second place for Spanish-English with an F1-score of 0.90. The HHU-UHG system introduces a novel unified neural network architecture for language identification in code-switched tweets for both Spanish-English and MSA-Egyptian dialect. The system makes use of word and character level representations to identify code-switching. For the MSA-Egyptian dialect the system does not rely on any kind of language-specific knowledge or linguistic resources such as, Part Of Speech (POS) taggers, morphological analyzers, gazetteers or word lists to obtain state-of-the-art performance.