Logical Reasoning
183 papers with code • 9 benchmarks • 11 datasets
Benchmarks
These leaderboards are used to track progress in Logical Reasoning
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Libraries
Use these libraries to find Logical Reasoning models and implementationsDatasets
BIG-bench
SMART-101
REBUS
FLD
Subtasks
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Navigate
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Novel Concepts
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Temporal Sequences
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StrategyQA
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StrategyQA
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Physical Intuition
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Date Understanding
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Elementary Mathematics
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Logic Grid Puzzle
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Logical Fallacy Detection
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Logical Sequence
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Epistemic Reasoning
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Analytic Entailment
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Checkmate In One
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Entailed Polarity
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Evaluating Information Essentiality
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Logical Args
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Metaphor Boolean
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Penguins In A Table
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Presuppositions As NLI
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Reasoning About Colored Objects
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Code Line Descriptions
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College Mathematics
Most implemented papers
Ontology Reasoning with Deep Neural Networks
This is an important and at the same time very natural logical reasoning task, which is why the presented approach is applicable to a plethora of important real-world problems.
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
jind11/MMM-MCQA
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Machine Reading Comprehension (MRC) for question answering (QA), which aims to answer a question given the relevant context passages, is an important way to test the ability of intelligence systems to understand human language.
Matrix Shuffle-Exchange Networks for Hard 2D Tasks
Convolutional neural networks have become the main tools for processing two-dimensional data.
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
Machine reading is a fundamental task for testing the capability of natural language understanding, which is closely related to human cognition in many aspects.
Measuring Systematic Generalization in Neural Proof Generation with Transformers
NicolasAG/SGinPG
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NeurIPS 2020
We observe that models that are not trained to generate proofs are better at generalizing to problems based on longer proofs.
Neural Software Analysis
The resulting tools complement and outperform traditional program analyses, and are used in industrial practice.
DAGN: Discourse-Aware Graph Network for Logical Reasoning
The model encodes discourse information as a graph with elementary discourse units (EDUs) and discourse relations, and learns the discourse-aware features via a graph network for downstream QA tasks.
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text
WangsyGit/LReasoner
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Findings (ACL) 2022
Logical reasoning of text requires understanding critical logical information in the text and performing inference over them.
Fact-driven Logical Reasoning for Machine Reading Comprehension
Recent years have witnessed an increasing interest in training machines with reasoning ability, which deeply relies on accurately and clearly presented clue forms.
Discriminative Reasoning for Document-level Relation Extraction
xwjim/DRN
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Findings (ACL) 2021
Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i. e., pattern recognition, logical reasoning, coreference reasoning, etc.)
