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A Neural Network Architecture for Multilingual Punctuation Generation
2016
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
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
doi:10.18653/v1/d16-1111
dblp:conf/emnlp/BallesterosW16
fatcat:xgntnaogljex5pynbp23gctvtm