This repo contains authors' original implementation for the paper Do Syntax Trees Help Pre-trained Transformers Extract Information? In this work, we explore different methods for adding syntactic structures into existing pretrained Transformer models, and evaluate their effectiveness on SRL, NER and relation extraction tasks.

• Download BERT/RoBERTa model weights from Huggingface by running the command

bash download_bert_model.sh

This script will create a directory "hf-bert-weights" and download the weights of four models. Warning: The size of the directory would

• Create a virtualenv named pyt using python3.6+ as

python3.6+ -m venv $HOME'/.virtualenvs/pyt'

Install the dependencies from the requirements.txt file as

pip install -r requirements.txt

• Include the CoNLL-2005 SRL task dataset under the datasets directory. As the distribution of this dataset is not freely allowed, for reference, this codebase includes 1000 lines for each files contained in the dataset.

• Now, run the provided script run_conll_srl.sh as:

bash run_conll_srl.sh

Also, make sure that the variable path settings in lines 12-21 of the above script are correct.

• In case of any confusions, errors or bugs, please report it in the Issues section.

• Install PyStanfordDependencies from my fork at: https://github.com/DevSinghSachan/PyStanfordDependencies This fork contains changes to use UniversalDependenciesConverter class in StanfordCoreNLPv4.0.0.

If you find this code useful, please consider citing our paper as:

@inproceedings{sachan-etal-2021-syntax,
    title = "Do Syntax Trees Help Pre-trained Transformers Extract Information?",
    author = "Sachan, Devendra  and
      Zhang, Yuhao  and
      Qi, Peng  and
      Hamilton, William L.",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eacl-main.228",
    doi = "10.18653/v1/2021.eacl-main.228"}