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https://github.com/THUDM/CogQA
Source code and dataset for ACL 2019 paper "Cognitive Graph for Multi-Hop Reading Comprehension at Scale"
https://github.com/THUDM/CogQA
bert graph-neural-networks question-answering
Last synced: about 1 month ago
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Source code and dataset for ACL 2019 paper "Cognitive Graph for Multi-Hop Reading Comprehension at Scale"
- Host: GitHub
- URL: https://github.com/THUDM/CogQA
- Owner: THUDM
- License: mit
- Created: 2019-05-29T04:54:51.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-03-31T14:41:37.000Z (over 1 year ago)
- Last Synced: 2024-11-14T06:31:43.819Z (about 1 month ago)
- Topics: bert, graph-neural-networks, question-answering
- Language: Python
- Homepage:
- Size: 35 MB
- Stars: 455
- Watchers: 19
- Forks: 82
- Open Issues: 13
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# CogQA
### [Project](https://sites.google.com/view/cognitivegraph/) | [arXiv](https://arxiv.org/abs/1905.05460)
Source codes for the paper **Cognitive Graph for Multi-Hop Reading Comprehension at Scale.** *(ACL 2019 Oral)*
We also have a [Chinese blog](https://zhuanlan.zhihu.com/p/72981392) about CogQA on Zhihu (η₯δΉ) besides the [paper](https://arxiv.org/abs/1905.05460).
## Introduction
CogQA is a novel framework for multi-hop question answering in **web-scale** documents. Founded on the dual process theory in cognitive science, CogQA gradually builds a *cognitive graph* in an iterative process by coordinating an implicit extraction module (System 1) and an explicit reasoning module (System 2). While giving accurate answers, our framework further provides **explainable** reasoning paths.
## Preprocess
1. Download and setup Redis database following https://redis.io/download
2. Download the dataset, evalute script and fullwiki data (enwiki-20171001-pages-meta-current-withlinks-abstracts) from https://hotpotqa.github.io. Unzip `improved_retrieval.zip` in this repo.
3. ``pip install -r requirements.txt``
4. Run ``python read_fullwiki.py`` to load wikipedia documents to redis (check the size of `dump.rdb` in the redis folder is about 2.4GB).
5. Run ``python process_train.py`` to generate `hotpot_train_v1.1_refined.json`, which contains edges in gold-only cognitive graphs.
6. ``mkdir models``
## Training
The codes automatic assign tasks on all available devices, each handling `batch_size / num_gpu` samples. We recommend that each gpu has at least 11GB memory to hold 2 batch.
1. Run `python train.py` to train Task #1(span extraction).
2. Run `python train.py --load=True --mode='bundle'` to train Task #2(answer prediction).
## Evaluation
The `cogqa.py` is the algorithm to answer questions with a trained model. We split the 1-hop nodes found by another similar model into `improved_retrieval.zip` for reuse in other algorithm. It can **directly** improve your result on fullwiki setting by just replacing the original input.
1. unzip ` improved_retrieval.zip`.
2. `python cogqa.py --data_file='hotpot_dev_fullwiki_v1_merge.json'`
3. `python hotpot_evaluate_v1.py hotpot_dev_fullwiki_v1_merge_pred.json hotpot_dev_fullwiki_v1_merge.json`
4. You can check the cognitive graph (reasoning process) in the `cg` part of the predicted json file.
## Notes
1. The changes of this version from the preview version is mainly about **detailed comments**.
2. The relatively sensetive hyperparameters includes the number of negative samples, top K, learning rate of task #2, scale factors between different parts...
3. If our work is useful to you, please cite our paper or star π our repo~~