Code and data for paper: WordTies: Measuring Word Associations in Language Models via Constrained Sampling (Yao et al., Findings 2022)

Please cite as:

@inproceedings{yao-etal-2022-wordties,
    title = "{W}ord{T}ies: Measuring Word Associations in Language Models via Constrained Sampling",
    author = "Yao, Peiran  and
      Renwick, Tobias  and
      Barbosa, Denilson",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.440",
    pages = "5959--5970"
}

Pipeline:

1. main.py is used for constrained sampling from MLMs
2. cooccur.py counts co-occurrences in sampled sentences.
3. find-assoc.py computes conditional probabilities and performs association rule mining.
4. evaluate.py calculates prec@k
5. evaluate-breakdown.py evaluates prec@k for different types of associations.
6. evaluate-asymmetry.py evaluates asymmetric associations.
7. stat-test.py performs statistical tests.
8. link-swow-and-kg.py Find the shortest paths that links cue and reponse in WordNet and ASCENT++.

In addition, contextual2static/ folder contains implementations of baselines.