Papers › MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning

MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning

1 Mar 2022Findings (ACL) 2022 5arXiv:2203.00357archive 2025-07-28

Fangkai Jiao, Yangyang Guo, Xuemeng Song, Liqiang Nie

Logical reasoning is of vital importance to natural language understanding. Previous studies either employ graph-based models to incorporate prior knowledge about logical relations, or introduce symbolic logic into neural models through data augmentation. These methods, however, heavily depend on annotated training data, and thus suffer from over-fitting and poor generalization problems due to the dataset sparsity. To address these two problems, in this paper, we propose MERIt, a MEta-path guided contrastive learning method for logical ReasonIng of text, to perform self-supervised pre-training on abundant unlabeled text data. Two novel strategies serve as indispensable components of our method. In particular, a strategy based on meta-path is devised to discover the logical structure in natural texts, followed by a counterfactual data augmentation strategy to eliminate the information shortcut induced by pre-training. The experimental results on two challenging logical reasoning benchmarks, i.e., ReClor and LogiQA, demonstrate that our method outperforms the SOTA baselines with significant improvements.

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AverageMeter sparkjiao/merit/models/roberta_sent_att_sup.py official repository ran Apache-2.0 (permissive) · bae3f62db60db88a · report
LogMixin sparkjiao/merit/models/roberta_sent_att_sup.py official repository ran Apache-2.0 (permissive) · 6a70f455b6a0d63e · report
extract_sent_tokens sparkjiao/merit/models/roberta_sent_att_sup.py official repository ran · our draft was wrong Apache-2.0 (permissive) · b90c2f73ba830814 · report
get_accuracy sparkjiao/merit/models/roberta_sent_att_sup.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 15b69fa520a0583e · report
get_child_logger sparkjiao/merit/models/roberta_sent_att_sup.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 477a33ae801770b3 · report
LogMetric sparkjiao/merit/models/roberta_sent_att_sup.py official repository unverified Apache-2.0 (permissive) · 490e26c4efc3d75f · report
RobertaSentForMultipleChoice sparkjiao/merit/models/roberta_sent_att_sup.py official repository unverified Apache-2.0 (permissive) · f81e954124a605c1 · report

Tasks

Contrastive LearningData AugmentationLogical ReasoningNatural Language UnderstandingReading Comprehension

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Reading Comprehension ReClor MERIt(MERIt-deberta-v2-xxlarge ) Test 79.3 #3 of 39 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Contrastive Learning

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