@inproceedings{li-etal-2025-logigraph,
    title = "{L}ogi{G}raph: Logical Reasoning with Contrastive Learning and Lightweight Graph Networks",
    author = "Li, Xiang  and
      Shi, Chen  and
      Xu, Yong  and
      Huang, Jun",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.72/",
    pages = "1069--1079",
    abstract = "Logical reasoning is a crucial factor in machine reading comprehension tasks (MRC). Existing methods suffer from the balance between semantic and explicit logical relation representations, in which some emphasize contextual semantics, while others pay more attention to explicit logical features. Additionally, previous methods utilize graph convolutional networks (GCN) for node updates, still exhibiting some shortcomings. To address these challenges, in this paper, we propose a logical reasoning method with contrastive learning and lightweight graph networks (LogiGraph). Our method focuses on the \textit{lightweight} aspect of the GCN, which greatly improves the shortcomings of the GCN, and employs conjunction and punctuation marks as two types of edges to construct a dual graph. Besides, we combine contrastive learning with graph reasoning, which changes the logical expression{'}s content as the negative sample of the original context, enabling the model to capture negative logical relationships and improving generalization ability. We conduct extensive experiments on two public datasets, ReClor and LogiQA. Experimental results demonstrate that LogiGraph can achieve state-of-the-art performance on both datasets."
}