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BERT for Joint Intent Classification and Slot Filling

BERT for Joint Intent Classification and Slot Filling

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

GitHub

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