Bridge

Making a bridge between NLP models and Brain data

This repository includes codes for:

• Comparing and evaluating representational spaces (e.g. internal states of language encoding models) using RSA and ReStA.
• Using internal states of a pretrained language model to predict brain activations.

Publication:

Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains

Requirements:

• tensorflow
• tensorflow_hub
• numpy
• sklearn
• spacy
• https://github.com/samiraabnar/GoogleLM1b.git
• https://github.com/google-research/bert

files:

Path to the raw brain data, e.g.: '/Users/samiraabnar/Codes/Data/harrypotter/'

Spacy model file for tokenization: python -m spacy download en_core_web_lg

How to obtain contextualized representations from different models:

python encode_stimuli_in_context.py

How to run predictive modelling experiments:

python run_experiment.py

How to run RSA experiments:

python rsa/compute_rep_sim.py

Link to colab for plotting the results:

https://colab.research.google.com/drive/1hO9ZV6gfO-WT-wGFwJZvHPQrLPHHuuhO#scrollTo=HeHmO4ZTL9O5&uniqifier=10

To create story_features.npy you can use the scripts in this colab notebook

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If you use our code, please consider citing our paper:

@inproceedings{abnar-etal-2019-blackbox,
    title = "Blackbox Meets Blackbox: Representational Similarity {\&} Stability Analysis of Neural Language Models and Brains",
    author = "Abnar, Samira  and
      Beinborn, Lisa  and
      Choenni, Rochelle  and
      Zuidema, Willem",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-4820",
}