Dialog System NLU

Tensorflow and Keras Implementation of the state of the art researches in Dialog System NLU. Tested on Tensorflow version 1.14.0

Implemented Papers

• BERT for Joint Intent Classification and Slot Filling

BERT for Joint Intent Classification and Slot Filling

Supported data format:

• Data format as in the paper Slot-Gated Modeling for Joint Slot Filling and Intent Prediction (Goo et al):
  • Consists of 3 files:
    • seq.in file contains text samples (utterances)
    • seq.out file contains tags corresponding to samples from seq.in
    • label file contains intent labels corresponding to samples from seq.in

Datasets included in the repo:

• Snips Dataset (Snips voice platform: an embedded spoken language understanding system for private- by-design voice interfaces )(Coucke et al., 2018), which is collected from the Snips personal voice assistant.
  • The training, development and test sets contain 13,084, 700 and 700 utterances, respectively.
  • There are 72 slot labels and 7 intent types for the training set.

Training the model with SICK-like data:

2 models implemented joint_bert and joint_bert_crf

Required Parameters:

• --train or -t Path to training data in Goo et al format.
• --val or -v Path to validation data in Goo et al format.
• --save or -s Folder path to save the trained model.

Optional Parameters:

• --epochs or -e Number of epochs.
• --batch or -bs Batch size.

python train_joint_bert.py --train=data/snips/train --val=data/snips/valid --save=saved_models/joint_bert_model --epochs=5 --batch=64

python train_joint_bert_crf.py --train=data/snips/train --val=data/snips/valid --save=saved_models/joint_bert_crf_model --epochs=5 --batch=32

Evaluating the Joint BERT NLU model:

Required Parameters:

• --model or -m Path to joint BERT NLU model.
• --data or -d Path to data in Goo et al format.

Optional Parameters:

• --batch or -bs Batch size.

python eval_joint_bert.py --model=saved_models/joint_bert_model --data=data/snips/test --batch=128

python eval_joint_bert_crf.py --model=saved_models/joint_bert_crf_model --data=data/snips/test --batch=128