wehub-resource-sync
45 lines
1.5 KiB
Markdown
45 lines
1.5 KiB
Markdown
# GraphWriter-DGL
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In this example we implement the GraphWriter, [Text Generation from Knowledge Graphs with Graph Transformers](https://arxiv.org/abs/1904.02342) in DGL. And the [author's code](https://github.com/rikdz/GraphWriter).
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## Dependencies
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- PyTorch >= 1.2
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- tqdm
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- pycoco (only for testing)
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- multi-bleu.perl and other scripts from mosesdecoder (only for testing)
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## Usage
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```
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# download data
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sh prepare_data.sh
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# training
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sh run.sh
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# testing
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sh test.sh
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```
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## Result on AGENDA
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| |BLEU|METEOR| training time per epoch|
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|Author's implementation|14.3+-1.01| 18.8+-0.28| 1970s|
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|DGL implementation|14.31+-0.34|19.74+-0.69| 1080s|
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We use the author's code for the speed test, and our testbed is V100 GPU.
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| |BLEU| detok BLEU| METEOR |
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|greedy, two layers| 13.97 +- 0.40| 13.78 +- 0.46| 18.76 +- 0.36|
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|beam 4, length penalty 1.0, two layers| 14.66 +- 0.65| 14.53 +- 0.52| 19.50 +- 0.49|
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|beam 4, length penalty 0.0, two layers| 14.33 +- 0.39| 14.09 +- 0.39| 18.63 +- 0.52|
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|greedy, six layers| 14.17 +- 0.46| 14.01 +- 0.51| 19.18 +- 0.49|
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|beam 4, length penalty 1.0, six layers| 14.31 +- 0.34| 14.35 +- 0.36| 19.74 +- 0.69|
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|beam 4, length penalty 0.0, six layers| 14.40 +- 0.85| 14.15 +- 0.84| 18.86 +- 0.78|
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We repeat the experiment five times.
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### Examples
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We also provide the output of our implementation on test set together with the reference text.
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- [GraphWriter's output](https://data.dgl.ai/models/graphwriter/tmp_pred.txt)
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- [Reference text](https://data.dgl.ai/models/graphwriter/tmp_gold.txt)
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