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
Implementation of Convolutional enhanced image Transformer

CeiT : Convolutional enhanced image Transformer This is an unofficial PyTorch implementation of Incorporating Convolution Designs into Visual Transfor

Rishikesh (ऋषिकेश) 82 Dec 13, 2022
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (Local-Lip)

Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (Local-Lip) Introduction TL;DR: We propose an efficient and trainabl

17 Dec 01, 2022
Unofficial implementation of MUSIQ (Multi-Scale Image Quality Transformer)

MUSIQ: Multi-Scale Image Quality Transformer Unofficial pytorch implementation of the paper "MUSIQ: Multi-Scale Image Quality Transformer" (paper link

41 Jan 02, 2023
A basic duplicate image detection service using perceptual image hash functions and nearest neighbor search, implemented using faiss, fastapi, and imagehash

Duplicate Image Detection Getting Started Install dependencies pip install -r requirements.txt Run service python main.py Testing Test with pytest How

Matthew Podolak 21 Nov 11, 2022
Code for the paper "Improved Techniques for Training GANs"

Status: Archive (code is provided as-is, no updates expected) improved-gan code for the paper "Improved Techniques for Training GANs" MNIST, SVHN, CIF

OpenAI 2.2k Jan 01, 2023
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Au

14 Nov 28, 2022
A curated list of awesome neural radiance fields papers

Awesome Neural Radiance Fields A curated list of awesome neural radiance fields papers, inspired by awesome-computer-vision. How to submit a pull requ

Yen-Chen Lin 3.9k Dec 27, 2022
Prml - Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop

Pattern Recognition and Machine Learning (PRML) This project contains Jupyter notebooks of many the algorithms presented in Christopher Bishop's Patte

Gerardo Durán-Martín 1k Jan 07, 2023
The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

PRIMER The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization. PRIMER is a pre-trained model for mu

AI2 114 Jan 06, 2023
(EI 2022) Controllable Confidence-Based Image Denoising

Image Denoising with Control over Deep Network Hallucination Paper and arXiv preprint -- Our frequency-domain insights derive from SFM and the concept

Images and Visual Representation Laboratory (IVRL) at EPFL 5 Dec 18, 2022
PyTorch implementation of ARM-Net: Adaptive Relation Modeling Network for Structured Data.

A ready-to-use framework of latest models for structured (tabular) data learning with PyTorch. Applications include recommendation, CRT prediction, healthcare analytics, and etc.

48 Nov 30, 2022
A Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Training Data》

RangeLoss Pytorch This is a Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Trai

Youzhi Gu 7 Nov 27, 2021
NAACL'2021: Factual Probing Is [MASK]: Learning vs. Learning to Recall

OptiPrompt This is the PyTorch implementation of the paper Factual Probing Is [MASK]: Learning vs. Learning to Recall. We propose OptiPrompt, a simple

Princeton Natural Language Processing 150 Dec 20, 2022
A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics, sequence features, and user profiles.

CCasGNN A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics,

5 Apr 29, 2022
Dataset and Code for ICCV 2021 paper "Real-world Video Super-resolution: A Benchmark Dataset and A Decomposition based Learning Scheme"

Dataset and Code for RealVSR Real-world Video Super-resolution: A Benchmark Dataset and A Decomposition based Learning Scheme Xi Yang, Wangmeng Xiang,

Xi Yang 92 Jan 04, 2023
Repository For Programmers Seeking a platform to show their skills

Programming-Nerds Repository For Programmers Seeking Pull Requests In hacktoberfest ❓ What's Hacktoberfest 2021? Hacktoberfest is the easiest way to g

42 Oct 29, 2022
Unofficial Tensorflow 2 implementation of the paper Implicit Neural Representations with Periodic Activation Functions

Siren: Implicit Neural Representations with Periodic Activation Functions The unofficial Tensorflow 2 implementation of the paper Implicit Neural Repr

Seyma Yucer 2 Jun 27, 2022
Realistic lighting in ursina!

Ursina Lighting Realistic lighting in ursina! If you want to have realistic lighting in ursina, import the UrsinaLighting.py in your project and use t

17 Jul 07, 2022
Multi-Scale Aligned Distillation for Low-Resolution Detection (CVPR2021)

MSAD Multi-Scale Aligned Distillation for Low-Resolution Detection Lu Qi*, Jason Kuen*, Jiuxiang Gu, Zhe Lin, Yi Wang, Yukang Chen, Yanwei Li, Jiaya J

DV Lab 115 Dec 23, 2022
Equivariant layers for RC-complement symmetry in DNA sequence data

Equi-RC Equivariant layers for RC-complement symmetry in DNA sequence data This is a repository that implements the layers as described in "Reverse-Co

7 May 19, 2022