R²F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference

This is the repository for our EMNLP 2022 paper: R²F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference

Prepare Dataset

Please download DOCNLI dataset.

Our complementary sentence-level annotation file is at here.

Run Model

To reproduce our results, please set appropriate file path parameters, and set do_train, do_eval, or do_predict as True for model training, evaluation, or prediction. Then for rouge retrieval (similar for other retrieval methods), please run

python rouge_retrieval_base.py

To conduct sentence-level evalaution, please set appropriate file path parameters. Then for rouge retrieval (similar for other retrieval methods), please run

python rouge_retrieval_base_sentence_evaluation.py

Checkpoint Files

Our checkpoint files for base encoder and large encoder are also released.

Contact

If you have any question about our work, please feel free to contact us at [email protected].

Citation

Please cite our work as {
title={R2F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference},
author={Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao},
booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
year={2022}
}