MTC: Multi Token Completion

This package provides code for the paper Simple and Effective Multi-Token Completion from Masked Language Models.

Paper: Simple and Effective Multi-Token Completion from Masked Language Models

Bibtex entry:

@inproceedings{DBLP:conf/eacl/KalinskyKLG23,
  author       = {Oren Kalinsky and
                  Guy Kushilevitz and
                  Alexander Libov and
                  Yoav Goldberg},
  title        = {Simple and Effective Multi-Token Completion from Masked Language Models},
  booktitle    = {Findings of the Association for Computational Linguistics: {EACL}
                  2023, Dubrovnik, Croatia, May 2-6, 2023},
  pages        = {2311--2324},
  publisher    = {Association for Computational Linguistics},
  year         = {2023},
  url          = {https://aclanthology.org/2023.findings-eacl.179},
  timestamp    = {Mon, 08 May 2023 14:38:37 +0200},
  biburl       = {https://dblp.org/rec/conf/eacl/KalinskyKLG23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Steps to run the code

Data

Download data from here.

Configuration

• pip install -r requirements.txt
• pip install transformers==4.5.1
• Update home_dir and data_path in configuration.py

Training

• RNN decoder - run mtc_model.py --input_path data/input_data_bert-base-cased_wiki_pub/
• EMAT decoder - run matrix_plugin.py --input_path data/input_data_bert-base-cased_wiki_pub/

Testing

• RNN decoder - run test.py --dataset_path data/input_data_bert-base-cased_wiki_pub/ --ckpt <CHECKPOINT_PATH> --all
• EMAT decoder - run matrix_plugin.py --input_path data/input_data_bert-base-cased_wiki_pub/ --ckpt <CHECKPOINT_PATH> --test
• T5 baseline - run T5_constrained_generation.py

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.