Dialog System NLU

Tensorflow and Keras Implementation of the state of the art researches in Dialog System NLU. Tested on Tensorflow version 1.15.0

Implemented Papers

• BERT for Joint Intent Classification and Slot Filling

BERT / ALBERT 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:

4 models implemented joint_bert and joint_bert_crf each supports bert and albert

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.
• --type or -tp to choose between bert and albert. Default is bert

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

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

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 --type=bert

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

Evaluating the Joint BERT / ALBERT NLU model:

Required Parameters:

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

Optional Parameters:

• --batch or -bs Batch size.
• --type or -tp to choose between bert and albert. Default is bert

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

python eval_joint_bert.py --model=saved_models/joint_albert_model --data=data/snips/test --batch=128 --type=albert

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

python eval_joint_bert_crf.py --model=saved_models/joint_albert_crf_model --data=data/snips/test --batch=128 --type=albert

Running a basic REST service for the Joint BERT / ALBERT NLU model:

Required Parameters:

• --model or -m Path to joint BERT / ALBERT NLU model.

Optional Parameters:

• --type or -tp to choose between bert and albert. Default is bert

python bert_nlu_basic_api.py --model=saved_models/joint_albert_model --type=albert

Sample request:

• POST
• Payload:

{
	"utterance": "make me a reservation in south carolina"
}

Sample Response:

{
	"intent": {
		"confidence": "0.9888",
		"name": "BookRestaurant"
	}, 
	"slots": [
	{
		"slot": "state",
		"value": "south carolina",
		"start": 5,
		"end": 6
	}
	]
}