/conversational-ai-workshop-18

Example showing generalisation

Primary LanguageJupyter Notebook

generalisation-example

This is the bot we used for the experiments in our paper on "Few-shot Generalisation Across Dialogue Tasks".

Installation

To install all the requirements needed to use this bot, please run:

pip install -r requirements.txt

Running the bot and experiments

To train any of the individual models, run one of:

  • The embedding policy:
make train-redp
  • The keras policy with a binary state featurizer
make train-lstm-bin
  • The keras policy with a label tokenizer state featurizer:
make train-lstm-feat

To run the bot on the commandline, run:

make run

To train the comparison models for the experiments from our paper, run:

make train-compare

To evaluate these models, run:

make evaluate-compare

Parameters used

Data:

  • exclusion percentages: [0, 5, 25, 50, 70, 90, 95, 100]
  • augmentation_factor: 0
  • runs: 5

Embedding policy:

  • epochs: 2000
  • attn_shift_range: 5
  • embed_dim: 20
  • both attentions
  • rnn size: 64
  • everthing else default

Keras policy:

  • binary featurizer/ label token featurizer
  • batch size: 32
  • rnn size: 64
  • epochs: 400
  • max history: 38

Overview of the files

data/core/train/ - training data from the hotel + restaurant domain data/core/test - test data from the hotel domain services - dummy API services for hotel/restaurant recommendation actions.py - actions file containing the hotel/restaurant search actions