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This code is for paper "Explainable Natural Language Inference via Identifying Important Rationales".

1. To run this code, you should obtain the data from https://github.com/OanaMariaCamburu/e-SNLI, and put the datas in the 'datas/raw_datas/' directory. Then process these data by run the 'process_data.py';

2. go to the file of 'rationale_extra' and run the train.py and then the predict.py to predict the rationales;

3. go to the file of 'rationales_sel' and run the main_selector.py to selector the rationales;

4. go to the file of 'generator' and

   4.1 finetune the GPT2 model: run the 'prepare_data_for_finetune.py' and 'GPT2_finetune_lm.py';

   4.2 generate the NLEs by the finetuned model: run the 'prepare_data_for_generation.py' and 'GPT2_generate.py';

5. go to the file of 'classify', and run the 'prepare_data_task_pred.py' to prepare data for train. Then, run the 'main_classify.py' to obtain the prediction results.

Note: All parameters use default values, and you can also set them at runtime.

After running the above code correctly, you will get the following files in the 'datas/snli_data_dir/' directory

 --train.json
 --dev.json
 --test.json
 --train-rationales.json
 --dev-rationales.json
 --test-rationales.json
 --train-with-hints.json
 --dev-with-hints.json
 --test-with-hints.json
 --train-prompts.txt  
 --train-prompts.txt
 --train-prompts.txt
 --train-finetune.txt
 --dev-finetune.txt
 --train-gen.txt
 --dev-gen.txt
 --test-gen.txt
 --train-r-with-e.txt
 --dev-r-with-e.txt
 --test-r-with-e.txt