Counterfactual-StoryRW

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Counterfactual-StoryRW

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code and data for EMNLP-19 paper "Counterfactual Story Reasoning and Generation" https://arxiv.org/abs/1909.04076

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# Dataset and code for "Counterfactual Story Reasoning and Generation"

This repo contains the dataset and code for the following paper:

[Counterfactual Story Reasoning and Generation](https://arxiv.org/abs/1909.04076)  
*Lianhui Qin, Antoine Bosselut, Ari Holtzman, Chandra Bhagavatula, Elizabeth Clark and Yejin Choi*  
EMNLP 2019

## Dataset: TimeTravel

The dataset can be **downloaded** from [here](https://drive.google.com/file/d/150jP5FEHqJD3TmTO_8VGdgqBftTDKn4w/view?usp=sharing).

**Data files includes** (see examples below):
1. `train_supervised_small.json`: supervised training set (the training set used in the paper experiments)
2. `train_supervised_large.json`: supervised training set (a *larger* supervised training set as we annotated more)
3. `train_unsupervised.json`: unsupervised training set
4. `dev_data.json`: Dev set
5. `test_data.json`: Test set

**Data format in each file**:

* Supervised training data example

```json
{
  "story_id": "4fd7d150-b080-4fb1-a592-8c27fa6e1fc8",
  "premise": "Andrea wanted a picture of her jumping.",
  "initial": "She set the camera up.",
  "counterfactual": "She asked her friend to draw one.",
  "original_ending": "Then, she jumped in the air. The picture kept coming out wrong. It took twenty tries to get it right.",
  "edited_ending": [
    "Then, she jumped in the air to demonstrate how she wanted it to look.",
    "The picture kept coming out wrong.",
    "It took drawing it several times to get it right."
  ]
}
```

* Unsupervised training data example

```json
{
  "story_id": "da0e85f1-c586-4236-a8a3-ee6421c8e71d",
  "premise": "Charles' mother taught her son to carry a pre-paid cell phone.",
  "initial": "As a job seeker, Charles put his cell phone number on applications.",
  "counterfactual": "As a job seeker, Charles used his cell phone to keep his information out of employers hands.",
  "original_ending": "He needed a real cell phone, but kept up with his pre-paid cell phone. One afternoon he was in a phone interview with Apple Computers. He ran out of minutes and never reached Apple's hiring manager again."
}
```

* Dev / test data example

```json
{
  "story_id": "048f5a77-7c17-4071-8b0b-b8e43087132d",
  "premise": "Neil was visiting Limerick in Ireland.",
  "initial": "There, he saw a beautiful sight.",
  "counterfactual": "It was the ugliest city he's ever seen.",
  "original_ending": "He saw the large and lovely River Shannon! After a few minutes, he agreed with the locals. The River Shannon was beautiful.",
  "edited_endings": [
    [
      "He saw the small and lonely River Shannon!",
      "After a few minutes, he agreed with the locals.",
      "The River Shannon was lonely."
    ],
    [
      "However, he saw the large and lovely River Shannon!",
      "After a few minutes, he agreed with the locals.",
      "The River Shannon was beautiful."
    ],
    [
      "However, he did think the large River Shannon was lovely!",
      "After a few minutes, he agreed with the locals that Limerick wasn't as ugly as he though.",
      "The River Shannon was beautiful."
    ]
  ]
}
```

## Code

* The code depends on [Texar](https://github.com/asyml/texar). Please install the version under [third_party/texar](./third_party/texar). Follow the installation instructions in the README there.
* Use `prepare_data_rewriting.py` to preprocess the raw text data and transform into TFRecord format. An example command is (please see the code for more config options).
```bash
python prepare_data_rewriting.py --data_dir=raw_data_dir
```
* Run `run_[X].sh` for training/testing model `[X]`.
* Use `evaluate.py` for evaluation. An example command is
```bash
python evaluate.py --all-preds-dir data/100_output_proced --gold-file data/dev.jsonl &> 100_output_proced_metrics.log
```
* The `WMS` and `W+SMS` metrics in the paper (Table.7) use the code [here](https://github.com/eaclark07/sms).

## Citation

```bibtex
@inproceedings{qin-counterfactual,
    title = "Counterfactual Story Reasoning and Generation",
    author = "Qin, Lianhui and Bosselut, Antoine and Holtzman, Ari and  Bhagavatula, Chandra and  Clark, Elizabeth and Choi, Yejin",
    booktitle = "2019 Conference on Empirical Methods in Natural Language Processing.",
    month = "nov",
    year = "2019",
    address = "Hongkong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/pdf/1909.04076.pdf",
}
```

Counterfactual-StoryRW

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