@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://aclanthology.org/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."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-etal-2025-logigraph">
<titleInfo>
<title>LogiGraph: Logical Reasoning with Contrastive Learning and Lightweight Graph Networks</title>
</titleInfo>
<name type="personal">
<namePart type="given">Xiang</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chen</namePart>
<namePart type="family">Shi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yong</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jun</namePart>
<namePart type="family">Huang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-01</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 31st International Conference on Computational Linguistics</title>
</titleInfo>
<name type="personal">
<namePart type="given">Owen</namePart>
<namePart type="family">Rambow</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Leo</namePart>
<namePart type="family">Wanner</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marianna</namePart>
<namePart type="family">Apidianaki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hend</namePart>
<namePart type="family">Al-Khalifa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Barbara</namePart>
<namePart type="given">Di</namePart>
<namePart type="family">Eugenio</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Steven</namePart>
<namePart type="family">Schockaert</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, UAE</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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 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.</abstract>
<identifier type="citekey">li-etal-2025-logigraph</identifier>
<location>
<url>https://aclanthology.org/2025.coling-main.72/</url>
</location>
<part>
<date>2025-01</date>
<extent unit="page">
<start>1069</start>
<end>1079</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T LogiGraph: Logical Reasoning with Contrastive Learning and Lightweight Graph Networks
%A Li, Xiang
%A Shi, Chen
%A Xu, Yong
%A Huang, Jun
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F li-etal-2025-logigraph
%X 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 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.
%U https://aclanthology.org/2025.coling-main.72/
%P 1069-1079
Markdown (Informal)
[LogiGraph: Logical Reasoning with Contrastive Learning and Lightweight Graph Networks](https://aclanthology.org/2025.coling-main.72/) (Li et al., COLING 2025)
ACL