EMNLP 2018 Update

Data and code related to our recent [EMNLP'18 paper] (https://arxiv.org/abs/1808.10012) is released on 31st Oct 2018.

**Code contributors: Bhavana Dalvi Mishra, Niket Tandon, Joel Grus

Detailed instructions to train your own ProStruct model can be found in: EMNLP18-README.md

To evaluate your model's predictions on the ProPara task (EMNLP'18), please Download the evaluator code from a separate leaderboard repository: https://github.com/allenai/aristo-leaderboard/tree/master/propara

ProPara leaderboard is now live at: https://leaderboard.allenai.org/propara


The ProPara dataset is designed to train and test comprehension of simple paragraphs describing processes, e.g., photosynthesis. We treat the comprehension task as that of predicting, tracking, and answering questions about how entities change during the process.

This repository contains code following three neural models developed at Allen Institute for Artificial Intelligence. These models are built using the PyTorch-based deep-learning NLP library, AllenNLP.

ProLocal and Proglobal are described in our NAACL'18 paper.

    Reasoning about Actions and State Changes by Injecting Commonsense Knowledge, Bhavana Dalvi Mishra, Lifu Huang, Niket Tandon, Wen-tau Yih, Peter Clark, NAACL 2018

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

ProStruct model is described in our EMNLP'18 paper:

    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

** Niket Tandon and Bhavana Dalvi Mishra contributed equally to this work.

Setup Instruction

  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

Download the dataset

You can download the ProPara dataset from


Train your own models

Detailed instructions are given in the following READMEs:

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

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

  title={Reasoning about Actions and State Changes by Injecting Commonsense Knowledge},
  author={Niket Tandon and Bhavana Dalvi Mishra and Joel Grus and Wen-tau Yih and Antoine Bosselut and Peter Clark},