JointBERT

(Unofficial) Pytorch implementation of JointBERT: BERT for Joint Intent Classification and Slot Filling

Model Architecture

Predict intent and slot at the same time from one BERT model (=Joint model)
total_loss = intent_loss + coef * slot_loss (Change coef with --slot_loss_coef option)
If you want to use CRF layer, give --use_crf option

Dependencies

python>=3.5
torch==1.4.0
transformers==2.7.0
seqeval==0.0.12
pytorch-crf==0.7.2

Dataset

	Train	Dev	Test	Intent Labels	Slot Labels
ATIS	4,478	500	893	21	120
Snips	13,084	700	700	7	72

The number of labels are based on the train dataset.
Add UNK for labels (For intent and slot labels which are only shown in dev and test dataset)
Add PAD for slot label

Training & Evaluation

$ python3 main.py --task {task_name} \
                  --model_type {model_type} \
                  --model_dir {model_dir_name} \
                  --do_train --do_eval \
                  --use_crf

# For ATIS
$ python3 main.py --task atis \
                  --model_type bert \
                  --model_dir atis_model \
                  --do_train --do_eval
# For Snips
$ python3 main.py --task snips \
                  --model_type bert \
                  --model_dir snips_model \
                  --do_train --do_eval

Prediction

$ python3 predict.py --input_file {INPUT_FILE_PATH} --output_file {OUTPUT_FILE_PATH} --model_dir {SAVED_CKPT_PATH}

Results

Run 5 ~ 10 epochs (Record the best result)
Only test with uncased model
ALBERT xxlarge sometimes can't converge well for slot prediction.

		Intent acc (%)	Slot F1 (%)	Sentence acc (%)
Snips	BERT	99.14	96.90	93.00
	BERT + CRF	98.57	97.24	93.57
	DistilBERT	98.00	96.10	91.00
	DistilBERT + CRF	98.57	96.46	91.85
	ALBERT	98.43	97.16	93.29
	ALBERT + CRF	99.00	96.55	92.57
ATIS	BERT	97.87	95.59	88.24
	BERT + CRF	97.98	95.93	88.58
	DistilBERT	97.76	95.50	87.68
	DistilBERT + CRF	97.65	95.89	88.24
	ALBERT	97.64	95.78	88.13
	ALBERT + CRF	97.42	96.32	88.69

Updates

2019/12/03: Add DistilBert and RoBERTa result
2019/12/14: Add Albert (large v1) result
2019/12/22: Available to predict sentences
2019/12/26: Add Albert (xxlarge v1) result
2019/12/29: Add CRF option
2019/12/30: Available to check sentence-level semantic frame accuracy
2019/01/23: Only show the result related with uncased model
2019/04/03: Update with new prediction code

JointBERT

Model Architecture

Dependencies

Dataset

Training & Evaluation

Prediction

Results

Updates

References