Data and code related to our recent [EMNLP'18 paper] (https://arxiv.org/abs/1808.10012) will be released here soon...

Reasoning about Actions and State Changes by Injecting Commonsense Knowledge, Niket Tandon, Bhavana Dalvi Mishra, Joel Grus, Wen-tau Yih, Antoine Bosselut, Peter Clark, EMNLP 2018

A repository of the state change prediction models used for evaluation in the Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension paper accepted to NAACL'18. It contains two models built using the PyTorch-based deep-learning NLP library, AllenNLP.

• ProLocal: A simple local model that takes a sentence and entity as input and predicts state changes happening to the entity.
• ProGlobal: A global model for state change prediction that takes entire paragraph and an entity as input and predicts the entity's state at every time-step in the paragraph.

These models can be trained and evaluated as described below.

1. Create the propara environment using Anaconda

conda create -n propara python=3.6

2. Activate the environment

source activate propara

3. Install the requirements in the environment:

pip install -r requirements.txt

4. Test installation

pytest -v

You can download the dataset used in the NAACL'18 paper from

 http://data.allenai.org/propara/

Example command to run eval script:

  python propara/eval/evalQA.py tests/fixtures/eval/para_id.test.txt tests/fixtures/eval/gold_labels.test.tsv tests/fixtures/eval/sample.model.test_predictions.tsv

If you find these models helpful in your work, please cite:

@inproceedings{proparNaacl2018,
     Author = { {Bhavana Dalvi, Lifu Huang}, Niket Tandon, Wen-tau Yih, Peter Clark},
     Booktitle = {NAACL},
     Title = {Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension},
     Year = {2018}
}

** Bhavana Dalvi and Lifu Huang contributed equally to this work.