To extract labels and rules automatically, run the following commands. For Chinese Rewrite dataset, replace data_preprocess_en with data_preprocess_zh. You can replace pipeline_canard.sh with either pipeline_mudoco.sh or pipeline.sh (for Rewrite).
From Google Drive.
tar -xzvf RaST_data.tar.gz
mv RaST_data/canard* data_preprocess_en
mv RaST_data/mudoco* data_preprocess_en
mv RaST_data/rewrite* data_preprocess_zhReturn to the root directory and modify line 22 of train.sh with the correct model directory in experiments/ that contains params.json. Then run sh train.sh <dataset>. The top-2 checkpoints will be saved in this given directory under the current epoch number (e.g., experiments/canard/05).
Download from these links:
tar -xzvf <checkpoint_tar> # e.g., canard21_03-16.tar.gz
mv <checkpoint_dir> experiments # e.g., canard21_03-16Modify line 16 of test.sh to point to the correct model directory. Then run the following command:
sh test.sh <dataset> <epoch_number> # e.g., sh test.sh canard 05The best-performing models per dataset are below.
| Dataset | Path |
|---|---|
| CANARD | experiments/canard21_03-16/05 |
| MuDoCo | experiments/mudoco21_03-16/19 |
| Rewrite | experiments/rewrite21_03_19/19 |
To evaluate on an existing checkpoint, modify line 16 of test.sh to point to the correct checkpoint directory. Note that the best epoch number per checkpoint can be found in the table above.