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Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision
This repo contains code for the paper:
Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision
Faeze Brahman, Vered Shwartz, Rachel Rudinger, and Yejin Choi.
AAAI 2021, link to paper
Extracting Salient Spans
Download the defeasible-snli dataset from here. Then follow the steps below:
First, train a classifier on defeasible-snli dataset using: sh run_clf.sh
Second, obtain attention scores for input sentences using: sh run_saliency_all.sh
Third, extract grammatical salient spans (noun and verb phrases) by: sh run_span_extractor_all.sh
Collecting Rationales
Follow the instruction to generate rationales from different resources:
- Vanilla LM:
Run the following commands to generate rationales from LM using the salient spans:
export DATA_DIR=<PATH_TO_DIR_WITH_EXTRACTED_SALIENT_SPANS>
export OUT_DIR="./output/lm_rationale"
python -m source.preprocessing.generate_rationale_from_lm \
--dataset ${DATA_DIR}/train_extracted_spans.jsonl \
--out_file ${OUT_DIR}/train_rationalized_gpt2-medium.jsonl \
--max_single_rationale_length 12 \
--max_pair_rationale_length 12 \
--p_sampling 0.35 \
--rationale_redundancy 20 \
--device 4 \
--lm gpt2-medium \
--dataset_type definf-snli
- KG-enhanced LM:
We use only the ConceptNet portion of the data provided here and post-train GPT2-M on them:
python -m source.generative.run_language_modeling \
--output_dir output/conceptnet_trained \
--model_type=gpt2-medium \
--model_name_or_path=gpt2-medium \
--train_data_file=$TRAIN_FILE \
--do_train \
--do_eval \
--eval_data_file=$TEST_FILE \
--num_train_epochs 2
Using the trained model to generate rationales given the salient spans:
python -m source.preprocessing.generate_from_KG_enhanced_lm \
--dataset ${DATA_DIR}/train_extracted_spans.jsonl \
--out_file ${OUT_DIR}/train_rationalized_gpt2-medium-enh.jsonl \
--lm gpt2-medium \
--model_name_or_path output/concepnet_trained/
--dataset_type definf-snli
--p_sampling 0.5 \
--temperature 0.7 \
--rationale_redundancy 5 \
--device 0 \
- COMeT:
Make sure you've installed the COMeT reimplementation from here. We used beam5 for decoding.
python source/preprocessing/generate_rationale_from_comet.py --dataset ./data/defeasible-snli/train.csv --out_file ./data/defeasible-snli/comet_supervision/train_rationalized_comet.jsonl --device 0
- NLI:
Here, we first fine-tune T5 in a WT5 format on e-snli dataset, in which we only used the instances that are labeled as "contradiction" and "entailement". Download the dataset from here, unzip and put it at data/e-snli/ folder.
First run the following command:
python -m source.generative.encoder_decoder \
--train_file data/e-snli/train.csv \
--eval_data_file data/e-snli/dev.csv \
--out_dir output/e-snli_t5_large \
--model_name_or_path t5-large \
--device 0 \
--do_train \
--do_eval \
--eval_during_train \
--save_steps 1000 \
--save_total_limit 1 \
--overwrite_cache \
--num_train_epochs 5 \
--logging_steps 5000 \
--gradient_accumulation_steps 8 \
--train_batch_size 16 \
--eval_batch_size 16 \
--task wt5_esnli
Generate rationales for the train set of definf dataset using the pretrained rationale generation model on e-snli:
python -m source.generative.generate_texts \
--in_file data/defeasible-snli/train.csv \
--out_file output/e-snli_t5_large/train_definf_rationalized_ft_esnli.jsonl \
--model_name_or_path output/e-snli-t5-large \
--beams 5 \
--task wt5_DI \
--device 0
- NLI w/ Highights:
Similarly, we train a variant of T5-based based model using (only) the salient spans in the premise and hypothesis as input:
python -m source.generative.encoder_decoder \
--train_file data/e-snli/train.csv \
--eval_data_file data/e-snli/dev.csv
--out_dir output/e-snli_t5_large_highlight \
--model_type t5_large \
--model_name_or_path t5-large \
--device 0 \
--do_train --do_eval \
--eval_during_train \
--save_steps 2000 \
--save_total_limit 1 \
--num_train_epochs 5 \
--logging_steps 5000 \
--gradient_accumulation_steps 8 \
--train_batch_size 32 \
--eval_batch_size 32 \
--task wt5_esnli_highlight
And generate rationales:
export DATA_DIR=<PATH_TO_DIR_WITH_EXTRACTED_SALIENT_SPANS>
export OUT_DIR="./output/e-snli_t5_large_highlight"
python -m source.generative.generate_texts \
--in_file ${DATA_DIR}/train_extracted_spans.jsonl \
--out_file ${OUT_DIR}/train_rationalized_esnli_highlight.jsonl \
--device 0 \
--model_name_or_path output/e-snli_t5_large_highlight \
--beams 5 \
--task wt5_DI_highlight
Filtering Rationales
Following the collection step, train a classifier as proxy on e-SNLI dataset: sh run_exnli.sh
The filter final rationales collected from all sources: run_esnli_instance_predict.sh
Final Rationale Generation Models
- train final rationale generation (on the filtered rationales collected from different sources)
Download and put the final_rationale folder in the data/ folder, then run:
python -m source.generative.encoder_decoder \
--train_file data/final_rationale/train.csv \
--eval_data_file data/final_rationale/dev.csv \
--out_dir output/final_rationale_bart-large \
--model_type bart-large \
--model_name_or_path bart-large \
--device 0 \
--do_train \
--save_steps 1000 \
--save_total_limit 1 \
--num_train_epochs 2 \
--logging_steps 2000 \
--gradient_accumulation_steps 8 \
--train_batch_size 64 \
--task rationale
NOTE: --model_type can be among ["gpt2-xl", "bart-large"], and --task can be among ["rationale", "multi", "update_rationale", "update_type_rationale"].
Then generate rationales using:
python -m source.generative.generate_texts \
--in_file data/final_rationale/test.csv \
--out_file output/rationale_bart-large/test_rationale_bart-large.jsonl \
--model_name_or_path output/rationale_bart-large \
--beams 5 \
--task rationale \
--device 0
How to cite?
@inproceedings{brahman2021rationalize,
title={Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision},
author={Faeze Brahman, Vered Shwartz, Rachel Rudinger and Yejin Choi},
booktitle={AAAI},
year={2021}
}