Is Incremental Structure Prediction Process Universal across Languages?: Revisiting Parsing Strategy through Speculation
This repository provides the experimental codes for the CONLL2025 paper Is Incremental Structure Prediction Process Universal across Languages?: Revisiting Parsing Strategy through Speculation.
Simply running uv run python ... automatically recovers the environment.
script/preprocess.py is used. Hyperparameters for preprocess are directly written in the script.
Run
uv run python -m scripts.preprocess
For full experiments, you need to modify the script and set the following variables:
data_base_dir: str = "PathToDirectory" # Set the directory where all datasets are placed.
debug: bool = False # Set False, the default is True.
script/train_and_eval.py is used. Hyperparameters are directly written in the script.
Run
uv run python -m scripts.train_and_eval
For full experiments, you need to modify the script and set the following variables:
base_dir: Path = Path("../tmp") # Set the directory where all results will be placed.
debug: bool = False # Set False, the default is True
num_parallel: int # Number of parallel process.
use_gpu: bool # Wether to use gpu or not.
gpu_ids: list[int] # List of gpus ids for parallel processing.
script/bst_strategy_table.py, scripts/plot_inference_results.py, and scripts/plot_validation_loss.py are used. Hyperparameters are directly written in the script.
Run
uv run python -m scripts.best_strategy_table
uv run python -m scripts.plot_inference_results
uv run python -m scripts.plot_validation_loss
For full experiments, you need to modify the script and set the following variables:
base_dir: Path = Path("../tmp") # Set the directory where all results will be placed.
debug: bool = False # Set False, the default is True
num_parallel: int # Number of parallel process.
use_gpu: bool # Wether to use gpu or not.
gpu_ids: list[int] # List of gpus ids for parallel processing.
This code is distributed under the MIT License.