• Corpus ID: 85531558

Multilevel Text Normalization with Sequence-to-Sequence Networks and Multisource Learning

@article{Ruzsics2019MultilevelTN,
  title={Multilevel Text Normalization with Sequence-to-Sequence Networks and Multisource Learning},
  author={Tatyana Ruzsics and Tanja Samard{\vz}i{\'c}},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.11340}
}
We define multilevel text normalization as sequence-to-sequence processing that transforms naturally noisy text into a sequence of normalized units of meaning (morphemes) in three steps: 1) writing normalization, 2) lemmatization, 3) canonical segmentation. These steps are traditionally considered separate NLP tasks, with diverse solutions, evaluation schemes and data sources. We exploit the fact that all these tasks involve sub-word sequence-to-sequence transformation to propose a systematic… 

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