Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning

• URL: http://arxiv.org/abs/2205.00731v1
• Date: Mon, 2 May 2022 08:34:59 GMT
• Title: Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
• Authors: Fangzhi Xu, Qika Lin, Jun Liu, Yudai Pan, Lingling Zhang
• Abstract summary: We propose an end-to-end model Logiformer which utilizes a two-branch graph transformer network for logical reasoning of text. We introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively. The reasoning process provides the interpretability by employing the logical units, which are consistent with human cognition.
• Score: 10.716971124214332
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine reading comprehension has aroused wide concerns, since it explores the potential of model for text understanding. To further equip the machine with the reasoning capability, the challenging task of logical reasoning is proposed. Previous works on logical reasoning have proposed some strategies to extract the logical units from different aspects. However, there still remains a challenge to model the long distance dependency among the logical units. Also, it is demanding to uncover the logical structures of the text and further fuse the discrete logic to the continuous text embedding. To tackle the above issues, we propose an end-to-end model Logiformer which utilizes a two-branch graph transformer network for logical reasoning of text. Firstly, we introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively. The logical graph models the causal relations for the logical branch while the syntax graph captures the co-occurrence relations for the syntax branch. Secondly, to model the long distance dependency, the node sequence from each graph is fed into the fully connected graph transformer structures. The two adjacent matrices are viewed as the attention biases for the graph transformer layers, which map the discrete logical structures to the continuous text embedding space. Thirdly, a dynamic gate mechanism and a question-aware self-attention module are introduced before the answer prediction to update the features. The reasoning process provides the interpretability by employing the logical units, which are consistent with human cognition. The experimental results show the superiority of our model, which outperforms the state-of-the-art single model on two logical reasoning benchmarks.

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