  We present LemmaTag, a featureless neural network architecture that jointly
generates part-of-speech tags and lemmas for sentences by using bidirectional
RNNs with character-level and word-level embeddings. We demonstrate that both
tasks benefit from sharing the encoding part of the network, predicting tag
subcategories, and using the tagger output as an input to the lemmatizer. We
evaluate our model across several languages with complex morphology, which
surpasses state-of-the-art accuracy in both part-of-speech tagging and
lemmatization in Czech, German, and Arabic.
