  Text normalization, defined as a procedure transforming non standard words to
spoken-form words, is crucial to the intelligibility of synthesized speech in
text-to-speech system. Rule-based methods without considering context can not
eliminate ambiguation, whereas sequence-to-sequence neural network based
methods suffer from the unexpected and uninterpretable errors problem. Recently
proposed hybrid system treats rule-based model and neural model as two cascaded
sub-modules, where limited interaction capability makes neural network model
cannot fully utilize expert knowledge contained in the rules. Inspired by
Flat-LAttice Transformer (FLAT), we propose an end-to-end Chinese text
normalization model, which accepts Chinese characters as direct input and
integrates expert knowledge contained in rules into the neural network, both
contribute to the superior performance of proposed model for the text
normalization task. We also release a first publicly accessible largescale
dataset for Chinese text normalization. Our proposed model has achieved
excellent results on this dataset.
