Source Code: The base code for this repository has been taken from https://github.com/allenai/propara

TechTrack

The TechTrack dataset is track properties of diverse set of entities in technical procedural documents. To understand the format of the dataset, refer to the ProPara dataset:

    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

These models are 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.
• Bert: A Bert-based classifier that takes as input a natural query and step text and builds a linear classifier on top of CLS embedding.

ProLocal and Bert models are described in our paper. The setups are also described in brief in dataset/README.md.

Setup Instruction

1. Create the techtrack environment using Anaconda

conda create -n techtrack python=3.7

2. Activate the environment

source activate techtrack

3. Install the requirements in the environment:

pip install -r requirements.txt

Train your own models

Detailed instructions are given in the following READMEs:

• ProLocal
• Bert

Make sure to place the data files for the desired setup from dataset/ to data/Inputs. Continue to Scripts to use section for more instructions.

Scripts to use

Use various scripts in the root folder to run training and testing of various models and datasets

• run_all.sh : train Bert model on all properties (including combined model)
• run_all_comp.sh : train all models with all-False rows included
• run_all_test.sh : train isOpened model and test on all folders
• run_bert_test.sh : train combined Bert model and test on all properties
• run_prolocal_all.sh : train ProLocal model for all properties

Helper script to move data files

For each setup i and model M the data files are in dataset/setup_{i}/{M}/ which has to be placed in data/Inputs/. This can also be done using the helper script transport_data.sh whose synopsis is:

./transport_data.sh setup-num model

where

• setup-num is the setup number from {1,2,3}.
• model is the model name. Choose from {bert,prolocal} for setup-num = 1 and {bert} for setup-num = 2, 3.

Data Processing Scripts

Use the scripts from "data processing scripts" folder to parse data from wikihow pages and parse Brat output files to usable dataset formats

• State change type dataset for ProLocal model
• Natural Query, step and change type dataset for Bert model

The raw subfolder also contains some un-documented, intermediate and raw scripts which need not be used but are present in case needed.