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This is a TripPy model trained on MultiWOZ 2.1 for use in ConvLab-3. This model predicts informable slots, requestable slots, general actions and domain indicator slots. Expected joint goal accuracy for MultiWOZ 2.1 is in the range of 55-56%.

For information about TripPy DST, refer to TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking.

The training and evaluation code is available at the official TripPy repository.

Training procedure

The model was trained on MultiWOZ 2.1 data via supervised learning using the TripPy codebase. MultiWOZ 2.1 data was loaded via ConvLab-3's unified data format dataloader. The pre-trained encoder is RoBERTa (base). Fine-tuning the encoder and training the DST specific classification heads was conducted for 10 epochs.

Training hyperparameters

python3 run_dst.py \
  --task_name="unified" \
  --model_type="roberta" \
  --model_name_or_path="roberta-base" \
  --dataset_config=dataset_config/unified_multiwoz21.json \
  --do_lower_case \
  --learning_rate=1e-4 \
  --num_train_epochs=10 \
  --max_seq_length=180 \
  --per_gpu_train_batch_size=24 \
  --per_gpu_eval_batch_size=32 \
  --output_dir=results \
  --save_epochs=2 \
  --eval_all_checkpoints \
  --warmup_proportion=0.1 \
  --adam_epsilon=1e-6 \
  --weight_decay=0.01 \
  --fp16 \
  --do_train \
  --predict_type=dummy \
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