Cross-domain Feature Selection for Language Identification
We show that transductive (cross-domain) learning is an important consideration in building a general-purpose language identification system, and develop a feature selection method that generalizes across domains. Our results demonstrate that our method provides improvements in transductive transfer learning for language identification. We provide an implementation of the method and show that our system is faster than popular standalone language identification systems, while maintaining competitive accuracy.
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