This repository contains the code for the University of Melbourne's submission (PTST-UoM) in SemEval 2021 Task 10: Source-Free Domain Adaptation for Semantic Processing, subtask time expression recognition.
@inproceedings{kurniawan2021a,
title = {{{PTST-UoM}} at {{SemEval-2021 Task}} 10: {{Parsimonious Transfer}} for {{Sequence Tagging}}},
shorttitle = {{{PTST-UoM}} at {{SemEval-2021 Task}} 10},
booktitle = {Proceedings of the 15th {{International Workshop}} on {{Semantic Evaluation}} ({{SemEval-2021}})},
author = {Kurniawan, Kemal and Frermann, Lea and Schulz, Philip and Cohn, Trevor},
year = {2021},
month = aug,
pages = {445--451},
doi = {10.18653/v1/2021.semeval-1.54},
url = {https://aclanthology.org/2021.semeval-1.54},
}
We recommend you to use conda package manager. Then, create a virtual environment with all the required dependencies with:
conda env create -n [env] -f environment.yml
Replace [env]
with your desired environment name. Once created, activate the environment. The
command above also installs the CPU version of PyTorch. If you need the GPU version, follow
the corresponding PyTorch installation docs afterwards. If you're using other package manager
(e.g., pip), you can look at the environment.yml
file to see what the requirements are.
Follow the instructions on the task's CodaLab site: https://competitions.codalab.org/competitions/26152#learn_the_details-getting-started-time
Assuming you have the practice data in the current working directory, run:
./run_ptst.py with best gold_path=practice_data corpus.path=practice_text
This command will train a model on the given practice text. To see other available options, run:
./run_ptst.py print_config
Script run_ptst.py
uses Sacred so that you can store all
about an experiment run in a MongoDB database. Simply set environment variables SACRED_MONGO_URL
to point to a MongoDB instance and SACRED_DB_NAME
to a database name to activate it. Also,
invoke the help command to print its usage, i.e. ./run_ptst.py help
.