The source code of Paper "Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text"

Our ensemble system is the first to surpass human performance on both EASY set and HARD set of ReClor (EvalAI leaderboard). If you find this paper useful, please cite this paper:

@misc{wang2021logicdriven,
      title={Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text}, 
      author={Siyuan Wang and Wanjun Zhong and Duyu Tang and Zhongyu Wei and Zhihao Fan and Daxin Jiang and Ming Zhou and Nan Duan},
      year={2021},
      eprint={2105.03659},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

1. To set up the environment, please install the packages in the requirements.txt.

pip install -r requirements.txt

2. To get the datasets, you can refer to the paper ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning to get the original data. We also provide our preprocessed data in reclor-data directory. Or you can start from the original data and preprocess it by the following steps:

• Step 1: exrtact the logical symbols and identify the logical expressions in the context, then infer the entailed logical expressions;
• Step 2: select a logical expreesion in the context and construct the negative samples based on it.

cd DataPreprocess
python extract_logical_expressions_v2.py
python construct_negative_samples_v2.py

Then you can run the LReasoner system in the scripts directory as following:

1. to run LReasoner_roberta
        bash run_roberta_DA_CE.sh
    
2. to run LReasoner_albert
        bash run_albert_DA_CE.sh

Here CE means context extension while DA means data augmentation.

We obtain the following results:

The checkpoints of LReasoner-RoBERTa and LReasoner-ALBERT can be accessed here: https://drive.google.com/drive/folders/1Gqoa7DR9wZmMhpLHuXQnznEhdnlZnkbp?usp=sharing.

We only evaluate our model on the ReClor dataset and plan to try more datasets. As codes are publicly available, anyone interested could also have a try.