This repository contains code for the EMNLP 2021 paper "Measuring Association Between Labels and Free-Text Rationales" by Sarah Wiegreffe, Ana Marasović and Noah A. Smith.

When using this code, please cite:

@inproceedings{wiegreffe-etal-2021-measuring,
    title = "{M}easuring Association Between Labels and Free-Text Rationales",
    author = "Wiegreffe, Sarah  and
      Marasovi{\'c}, Ana  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.804",
    pages = "10266--10284",
    abstract = "In interpretable NLP, we require faithful rationales that reflect the model{'}s decision-making process for an explained instance. While prior work focuses on extractive rationales (a subset of the input words), we investigate their less-studied counterpart: free-text natural language rationales. We demonstrate that *pipelines*, models for faithful rationalization on information-extraction style tasks, do not work as well on {``}reasoning{''} tasks requiring free-text rationales. We turn to models that *jointly* predict and rationalize, a class of widely used high-performance models for free-text rationalization. We investigate the extent to which the labels and rationales predicted by these models are associated, a necessary property of faithful explanation. Via two tests, *robustness equivalence* and *feature importance agreement*, we find that state-of-the-art T5-based joint models exhibit desirable properties for explaining commonsense question-answering and natural language inference, indicating their potential for producing faithful free-text rationales.",
}

environment.yml contains specifications for the conda environment needed to run the code. You will need pytorch=1.7.0, transformers=2.9.1, tokenizers=0.7.0, and nlp=0.4.0 (now datasets), which will also install other required packages. You will also need gitpython.

The code is relatively easily modified to work with newer version of transformers (and this results in higher performance in some cases, due to improvements in Huggingface's implementation of the T5Tokenizer). However, in order to exactly reproduce the paper's results, tokenizers=0.7.0 is needed, requiring an earlier version of the transformers package.

• To improve results further, you can update the minimum length of the generated sequences to 100 tokens or more (line 121 in custom_args.py). This is not currently done to preserve replicability of the paper's results.

• Training + Optional Inference: PYTHONPATH=. python input_to_label_and_rationale.py --output_dir [where_to_save_models] --task_name [esnli, cos_e] --do_train --num_train_epochs 200 --per_device_train_batch_size 64 --per_device_eval_batch_size 64 --logging_first_step True --logging_steps 1 --save_steps 1 --save_total_limit 11 --seed 42 --early_stopping_threshold 10 --version_name [for cos_e, specify v1.0 or v1.11]
  • evaluation options (can add any combination of these flags) to perform once training is complete: --do_eval --dev_predict --train_predict --test_predict
• Inference on a previously-trained model: PYTHONPATH=. python input_to_label_and_rationale.py --output_dir [where_to_save_models; nothing will be saved here] --task_name [esnli, cos_e] --pretrained_model_file [path to pretrained model directory] --per_device_eval_batch_size 64 --seed 42 --version_name [for cos_e, specify v1.0 or v1.11]
  • evaluation options (can add any combination of these flags): --do_eval --dev_predict --train_predict --test_predict
  • if you already have a file of generations in the pretrained model directory, you can specify that via the flag ---generations_filepath instead of specifying a --pretrained_model_file to load. This will save time by loading the generations from the file rather than having the model re-generate a prediction for each instance in the specified data split(s).
    • The above command changes to: PYTHONPATH=. python input_to_label_and_rationale.py --output_dir [where_to_save_models; nothing will be saved here] --task_name [esnli, cos_e] --generations_filepath [path to pretrained model directory]/checkpoint-[num]/[train/test/validation]_generations.txt --per_device_eval_batch_size 64 --seed 42 --version_name [for cos_e, specify v1.0 or v1.11]

• same as I-->OR model but with addition of --rationale_only flag.

• Training + Optional Inference: PYTHONPATH=. python rationale_to_label.py --output_dir [where_to_save_models] --task_name [esnli, cos_e] --do_train --num_train_epochs 200 --per_device_train_batch_size 64 --per_device_eval_batch_size 64 --logging_first_step True --logging_steps 1 --save_steps 1 --save_total_limit 11 --seed 42 --early_stopping_threshold 10 --use_dev_real_expls --version_name [for cos_e, specify v1.0 or v1.11]

  • evaluation options (can add any combination of these flags) to perform once training is complete: --do_eval --dev_predict --train_predict --test_predict
  • the model is always trained (and optionally evaluated) on ground-truth (dataset) explanations.

• Inference on a previously-trained model (also for evaluating model-generated explanations): PYTHONPATH=. python rationale_to_label.py --output_dir [where_to_save_models; nothing will be saved here] --task_name [esnli, cos_e] --pretrained_model_file [path to pretrained model directory] --per_device_eval_batch_size 64 --seed 42 --version_name [for cos_e, specify v1.0 or v1.11]

  • evaluation options (can add any combination of these flags): --do_eval --dev_predict --train_predict --test_predict
  • source of input explanations: specify either --use_dev_real_expls to use dataset explanations, or --predictions_model_file [path_to_pretrained_model_directory/checkpoint_x/train_posthoc_analysis{_1}.txt] to specify a file of predicted model explanations to use as inputs. Note the train_posthoc_analysis.txt does not have to exist, but the splits you are predicting on do (e.g. {train,test,validation}_posthoc_analysis.txt depending on which evaluation flags (--{train,dev_test}_predict) you've specified). The code will substitute these split names into the filepath passed in.
  • if you already have a file of generations in the pretrained model directory, you can specify that via the flag ---generations_filepath instead of specifying a --pretrained_model_file to load. This will save time by loading the generations from the file rather than having the model re-generate a prediction for each instance in the specified data split(s).
    • The above command changes to: PYTHONPATH=. python rationale_to_label.py --output_dir [where_to_save_models; nothing will be saved here] --task_name [esnli, cos_e] --generations_filepath [path to pretrained model directory]/checkpoint-[num]/[train/test/validation]_generations.txt --per_device_eval_batch_size 64 --seed 42 --version_name [for cos_e, specify v1.0 or v1.11]

• same as R-->O model but with addition of --include_input flag.
• Simulatability of a set of rationales is computed as IR-->O performance minus I-->O performance using the above "inference on a previously-trained model" command and specifying the set of rationales to pass in using --predictions_model_file.

• same as I-->OR model but with addition of --label_only flag.

• in progress

• in progress