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conversational-ai-workshop-18

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Example showing generalisation

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// repository documentation

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# generalisation-example
This is the bot we used for the experiments in our paper on "[Few-shot
Generalisation Across Dialogue Tasks"](https://arxiv.org/abs/1811.11707).

## 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

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