# Hierarchical Neural Story Generation (Fan et al., 2018)

The following commands provide an example of pre-processing data, training a model, and generating text for story generation with the WritingPrompts dataset.

## Pre-trained models

Description | Dataset | Model | Test set(s)
---|---|---|---
Stories with Convolutional Model <br> ([Fan et al., 2018](https://arxiv.org/abs/1805.04833)) | [WritingPrompts](https://arxiv.org/abs/1805.04833) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.bz2) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/stories_test.tar.bz2)

We provide sample stories generated by the [convolutional seq2seq model](https://dl.fbaipublicfiles.com/fairseq/data/seq2seq_stories.txt) and [fusion model](https://dl.fbaipublicfiles.com/fairseq/data/fusion_stories.txt) from [Fan et al., 2018](https://arxiv.org/abs/1805.04833).

## Dataset

The dataset can be downloaded like this:

```bash
cd examples/stories
curl https://dl.fbaipublicfiles.com/fairseq/data/writingPrompts.tar.gz | tar xvzf -
```

and contains a train, test, and valid split. The dataset is described here: https://arxiv.org/abs/1805.04833. We model only the first 1000 words of each story, including one newLine token.

## Example usage

First we will preprocess the dataset. Note that the dataset release is the full data, but the paper models the first 1000 words of each story. Here is example code that trims the dataset to the first 1000 words of each story:
```python
data = ["train", "test", "valid"]
for name in data:
    with open(name + ".wp_target") as f:
        stories = f.readlines()
    stories = [" ".join(i.split()[0:1000]) for i in stories]
    with open(name + ".wp_target", "w") as o:
        for line in stories:
            o.write(line.strip() + "\n")
```

Once we've trimmed the data we can binarize it and train our model:
```bash
# Binarize the dataset:
export TEXT=examples/stories/writingPrompts
fairseq-preprocess --source-lang wp_source --target-lang wp_target \
    --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
    --destdir data-bin/writingPrompts --padding-factor 1 --thresholdtgt 10 --thresholdsrc 10

# Train the model:
fairseq-train data-bin/writingPrompts -a fconv_self_att_wp --lr 0.25 --clip-norm 0.1 --max-tokens 1500 --lr-scheduler reduce_lr_on_plateau --decoder-attention True --encoder-attention False --criterion label_smoothed_cross_entropy --weight-decay .0000001 --label-smoothing 0 --source-lang wp_source --target-lang wp_target --gated-attention True --self-attention True --project-input True --pretrained False

# Train a fusion model:
# add the arguments: --pretrained True --pretrained-checkpoint path/to/checkpoint

# Generate:
# Note: to load the pretrained model at generation time, you need to pass in a model-override argument to communicate to the fusion model at generation time where you have placed the pretrained checkpoint. By default, it will load the exact path of the fusion model's pretrained model from training time. You should use model-override if you have moved the pretrained model (or are using our provided models). If you are generating from a non-fusion model, the model-override argument is not necessary.

fairseq-generate data-bin/writingPrompts --path /path/to/trained/model/checkpoint_best.pt --batch-size 32 --beam 1 --sampling --sampling-topk 10 --sampling-temperature 0.8 --nbest 1 --model-overrides "{'pretrained_checkpoint':'/path/to/pretrained/model/checkpoint'}"
```

## Citation
```bibtex
@inproceedings{fan2018hierarchical,
  title = {Hierarchical Neural Story Generation},
  author = {Fan, Angela and Lewis, Mike and Dauphin, Yann},
  booktitle = {Conference of the Association for Computational Linguistics (ACL)},
  year = 2018,
}
```
