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README.md

Leakage-Adjusted Simulatability

This is the codebase for the paper:
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?
Peter Hase, Shiyue Zhang, Harry Xie, Mohit Bansal. Findings of EMNLP 2020

Repository Structure

|__ sim_experiments/ --> Directory with models and experiment scripts
    |__ data/ --> includes eSNLI and CoS-E data used in experiments
    |__ models/ --> includes wrappers for T5 model for multiple choice tasks
    |__ training_reports/ --> includes wrappers for T5 model for multiple choice tasks
    |__ main.py --> train task model or simulator model on SNLI or CQA
    |__ T5-2-agent_main.py --> train models for multi-agent experiments [SGD] in paper
    |__ compute_sim.py --> script for computing LAS score for model explanations
    |__ run_tasks.py --> script for running experimental conditions across seeds
    |__ *.py --> utilities for data loading, explanation sampling, etc.
    |__ causal_estimation.Rmd --> R markdown script used to calibrate simulator models and compute LAS with *k* bins
|__ alternative_objectives/ --> Directory with additional experimental scripts
    |__ code coming soon
|__ rl_experiments/ --> Directory with code for multi-agent [RL] experimental scripts
    |__ see internal README
|__ human_experiments/ --> Directory with R code for analyzing human experiment data        
    |__ ratings_analysis.Rmd --> R markdown script used to analyze human quality ratings
    |__ expert_analysis.Rmd --> R markdown script used to analyze expert simulation data
    |__ more human experiment code coming soon
|__ requirements.txt

Requirements

  • Python 3.6
  • torch 1.4
  • transformers 2.5.1
  • sacrebleu
  • pandas
  • numpy

Reproducing Experiments

See READMEs in each directory for instructions on reproducing each set of experiments.

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Code for paper "Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?"

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