This is forked from Qipeng Guo's repository, and excludes large data files.
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
sh run.sh
sh test.sh
| BLEU | METEOR | training time per epoch | |
|---|---|---|---|
| paper | 14.3+-1.01 | 18.8+-0.28 | 1970s |
| this repo | 14.31+-0.34 | 19.74+-0.69 | 1192s |
We use the author's code for the speed test, and our testbed is V100 GPU.
| BLEU | detok BLEU | METEOR | |
|---|---|---|---|
| this repo, greedy, two layers | 13.97 +- 0.40 | 13.78 +- 0.46 | 18.76 +- 0.36 |
| this repo, beam 4, length penalty 1.0, two layers | 14.66 +- 0.65 | 14.53 +- 0.52 | 19.50 +- 0.49 |
| this repo, beam 4, length penalty 0.0, two layers | 14.33 +- 0.39 | 14.09 +- 0.39 | 18.63 +- 0.52 |
| this repo, greedy, six layers | 14.17 +- 0.46 | 14.01 +- 0.51 | 19.18 +- 0.49 |
| this repo, beam 4, length penalty 1.0, six layers | 14.31 +- 0.34 | 14.35 +- 0.36 | 19.74 +- 0.69 |
| this repo, 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.