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NLU Joint model for chinese with word embedding

What changed:

  1. Fix some bug in source.
  2. Add word embedding support, test passed on 64 size word2vec.
  3. Add predict function, already tested on test set.

Description:

  1. model.py -> NLP model with encoder and decoder.
  2. preprocess.py -> preprocessing raw data. including word segment, stop words removing.
  3. data.py -> utility class used to load training / test data from batch.
  4. train.py -> train data.
  5. service.py -> websocket service.
  6. main.py -> entrypoint for training / test / configuration

Things todo:

  1. Replace LSTM with GRU.
  2. Add F1 score and confusion matrix for accuracy.