# EntityDescriptionGeneration

**Repository Path**: silence98/EntityDescriptionGeneration

## Basic Information

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

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-04
- **Last Updated**: 2021-12-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Code Reference
[DCGCN](https://github.com/Cartus/DCGCN)

## Dependencies
The model requires:
- Python3
- [MXNet 1.3.0](https://github.com/apache/incubator-mxnet/tree/1.3.0)
- [Sockeye 1.18.56](https://github.com/awslabs/sockeye)
- CUDA

## Installation
#### GPU

If you want to run sockeye on a GPU you need to make sure your version of Apache MXNet
Incubating contains the GPU bindings. Depending on your version of CUDA you can do this by running the following:

```bash
> pip install -r requirements/requirements.gpu-cu${CUDA_VERSION}.txt
> pip install .
```
where `${CUDA_VERSION}` can be `75` (7.5), `80` (8.0), `90` (9.0), `91` (9.1), or `92` (9.2).

## ENT-DESC Dataset

The preprocessed ENT-DESC dataset is saved in `./sockeye/data`. For more details regarding the data preparation step, please refer to [ENT-DESC](https://github.com/LiyingCheng95/EntityDescriptionGeneration/tree/master/sockeye/data/ENT-DESC%20dataset).

Before that, we need to convert the raw dataset into multi graphs for training. For details please refer to the [paper](https://arxiv.org/pdf/2004.14813.pdf).

## Training

To train the DCGCN model, run:

```
./train.sh
```

Model checkpoints and logs will be saved to `./sockeye/model`.

## Decoding

When we finish the training, we can use the trained model to decode on the test set, run:

```
./decode.sh
```

This will use the last checkpoint by default. Use `--checkpoints` to specify a model checkpoint file.

## Evaluation

For BLEU score evaluation, run:

```
python3 -m sockeye.evaluate -r sockeye/data/ENT-DESC\ dataset/test_surface.pp.txt  -i sockeye/data/ENT-DESC\ dataset/test.snt.out
```

## Citation
```
@inproceedings{cheng2020ent,
  title={ENT-DESC: Entity Description Generation by Exploring Knowledge Graph},
  author={Cheng, Liying and Wu, Dekun and Bing, Lidong and Zhang, Yan and Jie, Zhanming and Lu, Wei and Si, Luo},
  booktitle={Proceedings of EMNLP},
  year={2020}
}
```