Even syntactically correct sentences are perceived as awkward if they do not contain correct punctuation. Still, the problem of automatic generation of punctuation marks has been largely neglected for a long time. We present a novel model that introduces punctuation marks into raw text material with transition-based algorithm using LSTMs. Unlike the state-of-the-art approaches, our model is language-independent and also neutral with respect to the intended use of the punctuation. Multilingual experiments show that it achieves high accuracy on the full range of punctuation marks across languages.
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