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We present LemmaTag, a featureless neural network architecture that jointlygenerates part-of-speech tags and lemmas for sentences by using bidirectionalRNNs with character-level and word-level embeddings. We demonstrate that bothtasks benefit from sharing the encoding part of the network, predicting tagsubcategories, and using the tagger output as an input to the lemmatizer. Weevaluate our model across several languages with complex morphology, whichsurpasses state-of-the-art accuracy in both part-of-speech tagging andlemmatization in Czech, German, and Arabic.

LemmaTag: Jointly Tagging and Lemmatizing for Morphologically-Rich Languages with BRNNs