Článek popisuje inovativní kombinaci recurrent neural-network based modelu na úrovni znaků a jazykového modelu aplikovanou na úlohu doplnění diakritiky do textu.
In this paper, we describe a novel combination of a character-level recurrent neural-network based model and a language model applied to diacritics restoration. In many cases in the past and still at present, people often replace characters with diacritics with their ASCII counterparts. Despite the fact that the resulting text is usually easy to understand for humans, it is much harder for further computational processing. This paper opens with a discussion of applicability of restoration of diacritics in selected languages. Next, we present a neural network-based approach to diacritics generation. The core component of our model is a bidirectional recurrent neural network operating at a character level. We evaluate the model on two existing datasets consisting of four European languages. When combined with a language model, our model reduces the error of current best systems by 20% to 64%. Finally, we propose a pipeline for obtaining consistent diacritics restoration datasets for twelve languages and evaluate our model on it. All the code is available under open source license on https://github.com/arahusky/diacritics_restoration.
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Nicoletta Calzolari; Khalid Choukri; Thierry Declerck; Bente Maegaard; Joseph Mariani; Hélène Mazo; Asunción Moreno; Jan Odijk; Stelios Piperidis