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metadata
language:
  - en
license: apache-2.0
tags:
  - dialogue policy
  - task-oriented dialog

lava-policy-multiwoz

This is the best performing LAVA_kl model from the LAVA paper which can be used as a word-level policy module in ConvLab3 pipeline.

Refer to ConvLab-3 for model description and usage.

Training procedure

The model was trained on MultiWOZ 2.0 data using the LAVA codebase. The model started with VAE pre-training and fine-tuning with informative prior KL loss, followed by corpus-based RL with REINFORCE.

Training hyperparameters

The following hyperparameters were used during SL training:

  • y_size: 10
  • k_size: 20
  • beta: 0.1
  • simple_posterior: true
  • contextual_posterior: false
  • learning_rate: 1e-03
  • max_vocab_size: 1000
  • max_utt_len: 50
  • max_dec_len: 30
  • backward_size: 2
  • train_batch_size: 128
  • seed: 58
  • optimizer: Adam
  • num_epoch: 100 with early stopping based on validation set

The following hyperparameters were used during RL training:

  • tune_pi_only: false
  • max_words: 100
  • temperature: 1.0
  • episode_repeat: 1.0
  • rl_lr: 0.01
  • momentum: 0.0
  • nesterov: false
  • gamma: 0.99
  • rl_clip: 5.0
  • random_seed: 38