  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 solution for all of them using neural
encoder-decoder technology. The specific challenge that we tackle in this paper
is integrating the traditional know-how on separate tasks into the neural
sequence-to-sequence framework to improve the state of the art. We address this
challenge by enriching the general framework with mechanisms that allow
processing the information on multiple levels of text organization (characters,
morphemes, words, sentences) in combination with structural information
(multilevel language model, part-of-speech) and heterogeneous sources (text,
dictionaries). We show that our solution consistently improves on the current
methods in all three steps. In addition, we analyze the performance of our
system to show the specific contribution of the integrating components to the
overall improvement.
