# Knowledge-Aware-Reader

**Repository Path**: chen_shu_ming/Knowledge-Aware-Reader

## Basic Information

- **Project Name**: Knowledge-Aware-Reader
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-29
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Code for the ACL 2019 paper:

## Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader

Paper link: [https://arxiv.org/abs/1905.07098](https://arxiv.org/abs/1905.07098)

Model Overview:
<p align="center"><img width="90%" src="assets/model.png" /></p>

### Requirements
* ``PyTorch 1.0.1``
* ``tensorboardX``
* ``tqdm``
* ``gluonnlp``

### Prepare data
```
mkdir datasets && cd datasets && wget https://sites.cs.ucsb.edu/~xwhan/datasets/webqsp.tar.gz && tar -xzvf webqsp.tar.gz && cd ..
```

### Full KB setting
```
CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_full_kb --max_num_neighbors 50 --label_smooth 0.1 --data_folder datasets/webqsp/full/ 
```

### Incomplete KB setting
Note: The Hits@1 should match or be slightly better than the number reported in the paper. More tuning on threshold should give you better F1 score. 
#### 30% KB
```
CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_kb_03 --max_num_neighbors 50 --use_doc --data_folder datasets/webqsp/kb_03/ --eps 0.05
```

#### 10% KB
```
CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_kb_01 --max_num_neighbors 50 --use_doc --data_folder datasets/webqsp/kb_01/ --eps 0.05
```
#### 50% KB
```
CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_kb_05 --num_layer 1 --max_num_neighbors 100 --use_doc --data_folder datasets/webqsp/kb_05/ --eps 0.05 --seed 3 --hidden_drop 0.05
```

### Citation
```
@inproceedings{xiong-etal-2019-improving,
    title = "Improving Question Answering over Incomplete {KB}s with Knowledge-Aware Reader",
    author = "Xiong, Wenhan  and
      Yu, Mo  and
      Chang, Shiyu  and
      Guo, Xiaoxiao  and
      Wang, William Yang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1417",
    doi = "10.18653/v1/P19-1417",
    pages = "4258--4264",
}
```