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(original sentence, logical equivalence sentence, logical inequivalence sentence). We use Abstract Meaning Representation (AMR) to automatically construct logical equivalence and logical inequivalence sentences. We use constrastive learning to train the model to learn to identify whether two sentences are logically equivalent or logically inequivalent. You are welcome to fine-tune the model weights on the dowstream tasks as logical reasoning reading comprehension tasks (ReClor and LogiQA) and natural language inference tasks (MNLI, MRPC, QNLI, RTE and QQP). We achieved #2 on the ReClor Leaderboard.(Contraposition law, Commutative law, Implication law, Double negation law) to construct 4 different logical equivalence/inequivalence sentences.(If A then B <=> If not B then not A) to show that following example is false.If Alice is happy, then Bob is smart.
If Alice is not happy, then Bob is smart.(If A then B <=> If not B then not A) to show that following example is true.If Alice is happy, then Bob is smart.
If Bob is not smart, then Alice is not happy.(A <=> not not A) to show that following example is false.Alice is happy.
Alice is not happy.(A <=> not not A) to show that following example is true.Alice is happy.
Alice is not sad.(If A then B <=> not A or B) to show that following example is false. The or in not A or B refer to the the meaning of otherwise in natural language.If Alan is kind, then Bob is clever.
Alan is kind or Bob is clever.(If A then B <=> not A or B) to show that following example is true. The or in not A or B refer to the the meaning of otherwise in natural language.If Alan is kind, then Bob is clever.
Alan is not kind or Bob is clever.(A and B <=> B and A) to show that following example is false.The bald eagle is clever and the wolf is fierce.
The wolf is not fierce and the bald eagle is not clever.(A and B <=> B and A) to show that following example is true.The bald eagle is clever and the wolf is fierce.
The wolf is fierce and the bald eagle is clever.from transformers import AutoModel
model = AutoModel.from_pretrained("qbao775/AMR-LE-DeBERTa-V2-XXLarge-Contraposition-Double-Negation-Implication-Commutative-Pos-Neg-1-2")
@article{bao2023contrastive,
title={Contrastive Learning with Logic-driven Data Augmentation for Logical Reasoning over Text},
author={Bao, Qiming and Peng, Alex Yuxuan and Deng, Zhenyun and Zhong, Wanjun and Tan, Neset and Young, Nathan and Chen, Yang and Zhu, Yonghua and Witbrock, Michael and Liu, Jiamou},
journal={arXiv preprint arXiv:2305.12599},
year={2023}
}