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Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging

Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging

Introduction

This is the code for Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging, published in the Findings of the Association for Computational Linguistics: EMNLP 2020.

The codes are written in Python 3.6.9 using the PyTorch framework in Jupyter Notebook format. The file names are similar to the names of the models introduced in the paper.

Requirements

You can find the required libraries in the requirements.txt file.

Citation

@inproceedings{doostmohammadi-etal-2020-persian,
    title = "{P}ersian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging",
    author = "Doostmohammadi, Ehsan  and
      Nassajian, Minoo  and
      Rahimi, Adel",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.86",
    doi = "10.18653/v1/2020.findings-emnlp.86",
    pages = "961--971",
}