ToMi

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This repository contains the code to generate the dataset used in our EMNLP 2019 paper: "Revisiting the Evaluation of Theory of Mind through Question Answering". The code is heavily based off of the following repository and paper

Generating the data

pip install tqdm
git clone git@github.com:facebookresearch/ToMi.git
cd ToMi
python main.py

This will produce six files in the data directory of the repository:

$ ls data
test.trace  test.txt  train.trace  train.txt  val.trace  val.txt

Data

The data follows the same format and uses the same models as the tom-qa-dataset repository. We do include one supplementary file for each *.txt file that classifies the story/question type in each example (which contains a .trace extension). Each line in a trace file contains a high level abstraction of the story as well as a classification of the question and a classification of the story. Story types can be one of:

1. true_belief - All agents observed all actions
2. false_belief - An agent failed to observe an action
3. second_order_false_belief - An agent has a false belief about another agent's set of beliefs

Question types can be one of:

1. first_order_(0|1)_tom - A first order false belief question in a story where a false belief situaion has been established
2. first_order_(0|1)_no_tom - A first order false belief question in a story where the agent in question observed all actions
3. second_order_(0|1)_tom - A second order false belief question in a story where a second order false belief situation has been established
4. second_order_(0|1)_no_tom - A second order false belief question in a story where the agent in question does not have a second order false belief
5. reality - A control question (ex: "Where is object x now?")
6. memory - A control question (ex: "Where was object x at the beginning?")

References

If you find this code useful for your research, please cite the following paper in your publication:

@inproceedings{le-etal-2019-revisiting,
    title = "Revisiting the Evaluation of Theory of Mind through Question Answering",
    author = "Le, Matthew  and
      Boureau, Y-Lan  and
      Nickel, Maximilian",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1598",
    doi = "10.18653/v1/D19-1598",
    pages = "5872--5877"
}

License

This code is licensed under CC-BY-NC 4.0.