In this example we implement the GraphWriter, Text Generation from Knowledge Graphs with Graph Transformers in DGL. And the author's code.
- PyTorch >= 1.2
- tqdm
- pycoco (only for testing)
- multi-bleu.perl and other scripts from mosesdecoder (only for testing)
# download data
sh prepare_data.sh
# training
sh run.sh
# testing
sh test.sh
| BLEU | METEOR | training time per epoch | |
|---|---|---|---|
| Author's implementation | 14.3+-1.01 | 18.8+-0.28 | 1970s |
| DGL implementation | 14.31+-0.34 | 19.74+-0.69 | 1080s |
We use the author's code for the speed test, and our testbed is V100 GPU.
| BLEU | detok BLEU | METEOR | |
|---|---|---|---|
| greedy, two layers | 13.97 +- 0.40 | 13.78 +- 0.46 | 18.76 +- 0.36 |
| beam 4, length penalty 1.0, two layers | 14.66 +- 0.65 | 14.53 +- 0.52 | 19.50 +- 0.49 |
| beam 4, length penalty 0.0, two layers | 14.33 +- 0.39 | 14.09 +- 0.39 | 18.63 +- 0.52 |
| greedy, six layers | 14.17 +- 0.46 | 14.01 +- 0.51 | 19.18 +- 0.49 |
| beam 4, length penalty 1.0, six layers | 14.31 +- 0.34 | 14.35 +- 0.36 | 19.74 +- 0.69 |
| beam 4, length penalty 0.0, six layers | 14.40 +- 0.85 | 14.15 +- 0.84 | 18.86 +- 0.78 |
We repeat the experiment five times.
We also provide the output of our implementation on test set together with the reference text.