Method and apparatus for training text normalization model, method and apparatus for text normalization

An apparatus for training a text normalization model, comprising:

• at least one processor;
• and
• a memory storing instructions, the instructions when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising:
• inputting input characters in an input character sequence corresponding to an input text into a recurrent neural network corresponding to a to-be-generated text normalization model successively;
• classifying each of the input characters by the recurrent neural network to obtain a predicted classification result of the input character sequence;
• and
• adjusting a parameter of the recurrent neural network based on the difference between the predicted classification result of the input character sequence and a tagged classification result of a normalized text of the input text,
• wherein the input character sequence corresponding to the input text is generated by:
• segmenting the input text according to a first preset granularity to obtain a first segmentation result;
• and
• tagging a non-word character having at least two normalization results in the first segmentation result to obtain the input character sequence.

A non-transitory computer-readable storage medium storing a computer program, the computer program when executed by one or more processors, causes the one or more processors to perform operations, the operations comprising:

• inputting input characters in an input character sequence corresponding to an input text into a recurrent neural network corresponding to a to-be-generated text normalization model successively;
• classifying each of the input characters by the recurrent neural network to obtain a predicted classification result of the input character sequence;
• and
• adjusting a parameter of the recurrent neural network based on the difference between the predicted classification result of the input character sequence and a tagged classification result of a normalized text of the input text,
• wherein the input character sequence corresponding to the input text is generated by:
• segmenting the input text according to a first preset granularity to obtain a first segmentation result;
• and
• tagging a non-word character having at least two normalization results in the first segmentation result to obtain the input character sequence.