# Commonsense-Path-Generator

**Repository Path**: wandehua/Commonsense-Path-Generator

## Basic Information

- **Project Name**: Commonsense-Path-Generator
- **Description**: 采用GPT-2作为常识路径生成器连接常识问答实体对，使用bert/Robert/Albert等作为文本编码器编码问答实体对，最后在关系网络上扩展了注意力机制，以融合生成路径中蕴含的结构化知识嵌入和文本编码器产生的非结构化知识嵌入，帮助选出最终的答案。
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-12
- **Last Updated**: 2022-05-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Path-Generator-QA 

This is a Pytorch implementation for the EMNLP 2020 (Findings) paper: 
Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering [[arxiv](https://arxiv.org/abs/2005.00691)][[project page](https://wangpf3.github.io/pathgen-project-page/)]

Code folders: 

(1) `learning-generator`: conduct path sampling and then train the path generator.

(2) `commonse-qa`: use the generator to generate paths and then train the qa system on task dataset.

(3) `A-Commonsense-Path-Generator-for-Connecting-Entities.ipynb`: The notebook illustrating how to use our proposed generator to connect a pair of entities
with a commonsense relational path. 

Part of this code and instruction rely on our another project [[code](https://github.com/INK-USC/MHGRN)][[arxiv](https://arxiv.org/abs/2005.00646)].  Please cite both of our works if you use this code. Thanks!
```
@article{wang2020connecting,
  title={Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering},
  author={Wang, Peifeng and Peng, Nanyun and Szekely, Pedro and Ren, Xiang},
  journal={arXiv preprint arXiv:2005.00691},
  year={2020}
}

@article{feng2020scalable,
  title={Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering},
  author={Feng, Yanlin and Chen, Xinyue and Lin, Bill Yuchen and Wang, Peifeng and Yan, Jun and Ren, Xiang},
  journal={arXiv preprint arXiv:2005.00646},
  year={2020}
}
```

## Dependencies

- Python >= 3.6
- PyTorch == 1.1
- transformers == 2.8.0
- dgl == 0.3 (GPU version)
- networkx == 2.3

Run the following commands to create a conda environment:

```bash
conda create -n pgqa python=3.6
source activate pgqa
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
pip install dgl-cu100
pip install transformers==2.8.0 tqdm networkx==2.3 nltk spacy==2.1.6
python -m spacy download en
```

## For training a path generator
```bash
cd learning-generator
cd data
unzip conceptnet.zip
cd ..
python sample_path_rw.py
```

After path sampling, shuffle the resulting data './data/sample_path/sample_path.txt'
and then split them into train.txt, dev.txt and test.txt by ratio of 0.9:0.05:0.05 under './data/sample_path/'

Then you can start to train the path generator by running
```bash
# the first arg is for specifying which gpu to use
./run.sh $gpu_device
```

The checkpoint of the path generator would be stored in './checkpoints/model.ckpt'. 
Move it to '../commonsense-qa/saved_models/pretrain_generator'.
So far, we are done with training the generator.

Alternatively, you can also download our well-trained path generator [checkpoint](https://drive.google.com/file/d/1dQNxyiP4g4pdFQD6EPMQdzNow9sQevqD/view?usp=sharing).

## For training a commonsense qa system

### 1. Download Data

First, you need to download all the necessary data in order to train the model:

```bash
cd commonsense-qa
bash scripts/download.sh
```

### 2. Preprocess

To preprocess the data, run:

```bash
python preprocess.py
```

### 3. Using the path generator to connect question-answer entities 
(Modify ./config/path_generate.config to specify the dataset and gpu device)

```bash
./scripts/run_generate.sh
```

### 4. Commonsense QA system training
```bash
bash scripts/run_main.sh ./config/csqa.config
```
Training process and final evaluation results would be stored in './saved_models/'