SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling

This repository contains code for reproducing experiments done in:

Ana Marasovic and Anette Frank (2018): SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). New Orleans, USA.

Requirements

• tensorflow 1.4
• nltk
• gensim
• matplotlib
• scikit-learn

Data

ORL

Download MPQA 2.0 corpus.

Check mpqa2-pytools for example usage.

Splits can be found in the datasplit folder.

SRL

The data is provided by: CoNLL-2005 Shared Task, but the original words are from the Penn Treebank dataset, which is not publicly available.

How to train models?

python main.py --adv_coef 0.0 --model fs --exp_setup_id new --n_layers_orl 0 --begin_fold 0 --end_fold 4

python main.py --adv_coef 0.0 --model html --exp_setup_id new --n_layers_orl 1 --n_layers_shared 2 --begin_fold 0 --end_fold 4

python main.py --adv_coef 0.0 --model sp --exp_setup_id new --n_layers_orl 3 --begin_fold 0 --end_fold 4

python main.py --adv_coef 0.1 --model asp --exp_setup_id prior --n_layers_orl 3 --begin_fold 0 --end_fold 10

Reference

If you make use of the contents of this repository, please cite the following paper:

@inproceedings{marasovicfrank:srl4orl,
  title={{Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling}},
  author={Marasovi\'{c}, Ana and Frank, Anette},
  booktitle={Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)},
  year={2018},
  address={New Orleans, USA},
  note={to appear}
}