Byte-based Language Identification with Deep Convolutional Networks

Johannes Bjerva

Byte-based Language Identification with Deep Convolutional Networks

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arxiv 1609.09004 v2 pith:JKTHMVPA submitted 2016-09-28 cs.CL

Johannes Bjerva This is my paper · ORCID

classification cs.CL

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We report on our system for the shared task on discriminating between similar languages (DSL 2016). The system uses only byte representations in a deep residual network (ResNet). The system, named ResIdent, is trained only on the data released with the task (closed training). We obtain 84.88% accuracy on subtask A, 68.80% accuracy on subtask B1, and 69.80% accuracy on subtask B2. A large difference in accuracy on development data can be observed with relatively minor changes in our network's architecture and hyperparameters. We therefore expect fine-tuning of these parameters to yield higher accuracies.

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