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/npresent 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.

This work was supported by the European Commission under the contract numbers FP7-ICT-/n610411 (MULTISENSOR) and H2020-RIA-645012 (KRISTINA).