Skip to content

Latest commit

 

History

History
27 lines (20 loc) · 1.72 KB

README.md

File metadata and controls

27 lines (20 loc) · 1.72 KB
Raw

Code for the paper "How Relevant Are Selectional Preferences for Transformer-based Language Models?"

Prerequisites

  • Python 3
  • Download the SP-10K corpus: git clone https://github.com/HKUST-KnowComp/SP-10K
  • transformers library: pip install transformers

Contents

  • extract_sents_from_corpus.py Extract sentences from the ukWaC corpus. You don't need to run this script if you already have the sentence files. These sentence files can be shared privately upon request, for copyright reasons. Arguments (required):

    • --corpus_dir the directory where the original corpus is (e.g. ukWaC corpus)
    • --sp_dir the directory where the SP-10K corpus is downloaded.

  • bert_analyze_sents.py Analyze sentences with BERT: probabilities, predictions, attention. You don't need to run this script if there is a results/[type_phrase] directory (you can find it in osirim: https://jupyter-slurm.irit.fr/user/emetheni/lab/tree/BERT_SP10K_experiments/results). Arguments (required):

    • --type_phrases the type of syntactic relations to create analysis for
    • --sent_file the file with preprocessed sentences
    • (optional) --bert_model the model to be used, default is bert-base-uncased.
    • (optional) --type_attention the types of attention mask to be used, defauls is standard i.e. no mask. context is for blocking everything but the head word, head is to block the head word, control blocks everything.

Citation

Metheniti, Eleni, Tim Van de Cruys and Nabil Hathout. (2020) How Relevant Are Selectional Preferences for Transformer-based Language Models? 28th International Conference on Computational Linguistics (COLING). (to appear)