scape1989 / prn

Procedural Reasoning Networks

Procedural Reasoning Networks for Understanding Multimodal Procedures

Mustafa Sercan Amac, Semih Yagcioglu, Aykut Erdem, Erkut Erdem

This is the implementation of Procedural Reasoning Networks for Understanding Multimodal Procedures (CoNLL 2019) on a RecipeQA dataset: RecipeQA dataset . We propose Procedural Reasoning Networks (PRN) to address the problem of comprehending procedural commonsense knowledge. See our website for more information about the model!

Bibtex

For PRN:

@inproceedings{prn2019,
  title={Procedural Reasoning Networks for Understanding Multimodal Procedures},
  author={Amac, Mustafa Sercan and Yagcioglu, Semih and Erdem, Aykut and Erdem, Erkut},
  booktitle={Proceedings of the CoNLL 2019},
  year={2019}
}

For RecipeQA dataset:

@inproceedings{yagcioglu-etal-2018-recipeqa,
   title = “{R}ecipe{QA}: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes”,
   author = “Yagcioglu, Semih  and
     Erdem, Aykut  and
     Erdem, Erkut  and
     Ikizler-Cinbis, Nazli”,
   booktitle = “Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing”,
   year = “2018",
   address = “Brussels, Belgium”,
   publisher = “Association for Computational Linguistics”,
   url = “https://www.aclweb.org/anthology/D18-1166“,
   doi = “10.18653/v1/D18-1166”,
   pages = “1358--1368",
}

Requirements

• You would need python3.6 or python3.7
• See requirements.txt for the required python packages and run pip install -r requirements.txt to install them.

Pre-processing

The code will automatically download pre-trained features and start the pre-processing procedure.

Training

To train the model, run the following command:

allennlp train config_file -s  directory_to_save --include-package recipeqalib

Example

We prepared 2 example config files. One of them is for single-task training, and the other one is for multi-task training. For training the single-task model run the following command:

allennlp train ./configs/example_single_task.json -s ./save/example_single_task --include-package recipeqalib

For training the multi-task model run the following command:

allennlp train ./configs/example_multi_task.json -s ./save/example_multi_task --include-package recipeqalib

Evaluation

In order to evaluate the trained model you would need to test image features and the test set questions. You can download them with the following script.

wget https://vision.cs.hacettepe.edu.tr/files/recipeqa/test.json ./data/test.json
wget https://vision.cs.hacettepe.edu.tr/files/recipeqa/test_img_features.pkl ./data/test_img_features.pkl

For a step-by-step evaluation example please see the evaluate_model notebook under notebooks folder.