On a new 144,000-word annotated dataset for Wolayta and Gofa, BERT-base-uncased embeddings with an LSTM classifier reach 0.72 F1, the best of seven compared approaches.
Byte-based Language Identification with Deep Convolutional Networks
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abstract
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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Bilingual Word Level Language Identification for Omotic Languages
On a new 144,000-word annotated dataset for Wolayta and Gofa, BERT-base-uncased embeddings with an LSTM classifier reach 0.72 F1, the best of seven compared approaches.