Generative Adversarial Network-Based Language Identification for Closely Related Same Language Family

The discrimination between similar languages is one of the main challenges in automatic language identification. In this paper, we address this problem by proposing a generative adversarial network-based language identification method for identifying the sentences from closely related languages of same language family. The proposed method works on dual-reward feedback learning comprising of generator to generate nearly close language sentences, discriminator for determining how similar the generated sentences are to that of the training data and classifier for optimal prediction of the correct label. We evaluate the proposed model for pairs of languages and overall testing data comparison on Indo-Aryan languages dataset [12]. The effectiveness of our method is demonstrated in comparison to other existing state-of-the-art methods.