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Parsimonious Transfer for Sequence Tagging

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.


  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 = {},

Installing requirements

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.

Get the data

Follow the instructions on the task's CodaLab site:

Running PTST

Assuming you have the practice data in the current working directory, run:

./ 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:

./ print_config

Sacred: an experiment manager

Script 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. ./ help.


PTST-UoM at SemEval 2021 Task 10: Source-Free Domain Adaptation for Semantic Processing







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