QA-GNN: Question Answering using Language Models and Knowledge Graphs

Overview

QA-GNN: Question Answering using Language Models and Knowledge Graphs

This repo provides the source code & data of our paper: QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering (NAACL 2021).

@InProceedings{yasunaga2021qagnn,
  author =  {Michihiro Yasunaga and Hongyu Ren and Antoine Bosselut and Percy Liang and Jure Leskovec},
  title =   {QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering},
  year =    {2021},  
  booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)},  
}

Webpage: https://snap.stanford.edu/qagnn

Usage

0. Dependencies

Run the following commands to create a conda environment (assuming CUDA10.1):

conda create -n qagnn python=3.7
source activate qagnn
pip install numpy==1.18.3 tqdm
pip install torch==1.4.0 torchvision==0.5.0
pip install transformers==2.0.0 nltk spacy==2.1.6
python -m spacy download en

#for torch-geometric
pip install torch-scatter==2.0.4 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html
pip install torch-cluster==1.5.4 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html
pip install torch-sparse==0.6.1 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html
pip install torch-spline-conv==1.2.0 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html
pip install torch-geometric==1.6.0 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html

1. Download Data

Download all the raw data -- ConceptNet, CommonsenseQA, OpenBookQA -- by

./download_raw_data.sh

You can preprocess the raw data by running

python preprocess.py -p <num_processes>

The script will:

  • Setup ConceptNet (e.g., extract English relations from ConceptNet, merge the original 42 relation types into 17 types)
  • Convert the QA datasets into .jsonl files (e.g., stored in data/csqa/statement/)
  • Identify all mentioned concepts in the questions and answers
  • Extract subgraphs for each q-a pair

TL;DR. The preprocessing may take long; for your convenience, you can download all the processed data by

./download_preprocessed_data.sh

The resulting file structure will look like:

.
├── README.md
└── data/
    ├── cpnet/                 (prerocessed ConceptNet)
    └── csqa/
        ├── train_rand_split.jsonl
        ├── dev_rand_split.jsonl
        ├── test_rand_split_no_answers.jsonl
        ├── statement/             (converted statements)
        ├── grounded/              (grounded entities)
        ├── graphs/                (extracted subgraphs)
        ├── ...

2. Training

For CommonsenseQA, run

./run_qagnn__csqa.sh

For OpenBookQA, run

./run_qagnn__obqa.sh

As configured in these scripts, the model needs two types of input files

  • --{train,dev,test}_statements: preprocessed question statements in jsonl format. This is mainly loaded by load_input_tensors function in utils/data_utils.py.
  • --{train,dev,test}_adj: information of the KG subgraph extracted for each question. This is mainly loaded by load_sparse_adj_data_with_contextnode function in utils/data_utils.py.

Use Your Own Dataset

  • Convert your dataset to {train,dev,test}.statement.jsonl in .jsonl format (see data/csqa/statement/train.statement.jsonl)
  • Create a directory in data/{yourdataset}/ to store the .jsonl files
  • Modify preprocess.py and perform subgraph extraction for your data
  • Modify utils/parser_utils.py to support your own dataset

Acknowledgment

This repo is built upon the following work:

Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering. Yanlin Feng*, Xinyue Chen*, Bill Yuchen Lin, Peifeng Wang, Jun Yan and Xiang Ren. EMNLP 2020.
https://github.com/INK-USC/MHGRN

Many thanks to the authors and developers!

Owner
Michihiro Yasunaga
PhD Student in Computer Science
Michihiro Yasunaga
Breast Cancer Detection 🔬 ITI "AI_Pro" Graduation Project

BreastCancerDetection - This program is designed to predict two severity of abnormalities associated with breast cancer cells: benign and malignant. Mammograms from MIAS is preprocessed and features

6 Nov 29, 2022
Codes for “A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection”

DSAMNet The pytorch implementation for "A Deeply-supervised Attention Metric-based Network and an Open Aerial Image Dataset for Remote Sensing Change

Mengxi Liu 41 Dec 14, 2022
GT China coal model

GT China coal model The full version of a China coal transport model with a very high spatial reslution. What it does The code works in a few steps: T

0 Dec 13, 2021
[NeurIPS2021] Code Release of Learning Transferable Perturbations

Learning Transferable Adversarial Perturbations This is an official release of the paper Learning Transferable Adversarial Perturbations. The code is

Krishna Kanth 17 Nov 11, 2022
PyTorch implementation of some learning rate schedulers for deep learning researcher.

pytorch-lr-scheduler PyTorch implementation of some learning rate schedulers for deep learning researcher. Usage WarmupReduceLROnPlateauScheduler Visu

Soohwan Kim 59 Dec 08, 2022
[MICCAI'20] AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes

AlignShift NEW: Code for our new MICCAI'21 paper "Asymmetric 3D Context Fusion for Universal Lesion Detection" will also be pushed to this repository

Medical 3D Vision 42 Jan 06, 2023
Official implementation of the paper ``Unifying Nonlocal Blocks for Neural Networks'' (ICCV'21)

Spectral Nonlocal Block Overview Official implementation of the paper: Unifying Nonlocal Blocks for Neural Networks (ICCV'21) Spectral View of Nonloca

91 Dec 14, 2022
Repo for our ICML21 paper Unsupervised Learning of Visual 3D Keypoints for Control

Unsupervised Learning of Visual 3D Keypoints for Control [Project Website] [Paper] Boyuan Chen1, Pieter Abbeel1, Deepak Pathak2 1UC Berkeley 2Carnegie

Boyuan Chen 34 Jul 22, 2022
This repo in the implementation of EMNLP'21 paper "SPARQLing Database Queries from Intermediate Question Decompositions" by Irina Saparina, Anton Osokin

SPARQLing Database Queries from Intermediate Question Decompositions This repo is the implementation of the following paper: SPARQLing Database Querie

Yandex Research 20 Dec 19, 2022
Official implementation for "Symbolic Learning to Optimize: Towards Interpretability and Scalability"

Symbolic Learning to Optimize This is the official implementation for ICLR-2022 paper "Symbolic Learning to Optimize: Towards Interpretability and Sca

VITA 8 Dec 19, 2022
Python-based Informatics Kit for Analysing Chemical Units

INSTALLATION Python-based Informatics Kit for the Analysis of Chemical Units Step 1: Make a conda environment: conda create -n pikachu python=3.9 cond

47 Dec 23, 2022
PyTorch implementation of the paper Deep Networks from the Principle of Rate Reduction

Deep Networks from the Principle of Rate Reduction This repository is the official PyTorch implementation of the paper Deep Networks from the Principl

459 Dec 27, 2022
Code for "Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation". [AAAI 2021]

Graph Evolving Meta-Learning for Low-resource Medical Dialogue Generation Code to be further cleaned... This repo contains the code of the following p

Shuai Lin 29 Nov 01, 2022
Dogs classification with Deep Metric Learning using some popular losses

Tsinghua Dogs classification with Deep Metric Learning 1. Introduction Tsinghua Dogs dataset Tsinghua Dogs is a fine-grained classification dataset fo

QuocThangNguyen 45 Nov 09, 2022
Data & Code for ACCENTOR Adding Chit-Chat to Enhance Task-Oriented Dialogues

ACCENTOR: Adding Chit-Chat to Enhance Task-Oriented Dialogues Overview ACCENTOR consists of the human-annotated chit-chat additions to the 23.8K dialo

Facebook Research 69 Dec 29, 2022
Official implementation of the paper 'Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution' in CVPR 2022

LDL Paper | Supplementary Material Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution Jie Liang*, Hu

150 Dec 26, 2022
Raptor-Multi-Tool - Raptor Multi Tool With Python

Promises 🔥 20 Stars and I'll fix every error that there is 50 Stars and we will

Aran 44 Jan 04, 2023
LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

LSTC: Boosting Atomic Action Detection with Long-Short-Term Context This Repository contains the code on AVA of our ACM MM 2021 paper: LSTC: Boosting

Tencent YouTu Research 9 Oct 11, 2022
Single Red Blood Cell Hydrodynamic Traps Via the Generative Design

Rbc-traps-generative-design - The generative design for single red clood cell hydrodynamic traps using GEFEST framework

Natural Systems Simulation Lab 4 Jun 16, 2022
Implementation of Hierarchical Transformer Memory (HTM) for Pytorch

Hierarchical Transformer Memory (HTM) - Pytorch Implementation of Hierarchical Transformer Memory (HTM) for Pytorch. This Deepmind paper proposes a si

Phil Wang 63 Dec 29, 2022