# Logical Reasoning

61 papers with code • 0 benchmarks • 0 datasets

## Benchmarks

These leaderboards are used to track progress in Logical Reasoning
## Subtasks

- Navigate
- Temporal Sequences
- Novel Concepts
- Physical Intuition
- Physical Intuition
- Date Understanding
- Logical Fallacy Detection
- Logical Sequence
- StrategyQA
- Elementary Mathematics
- Analytic Entailment
- Code Line Descriptions
- Checkmate In One
- Entailed Polarity
- Epistemic Reasoning
- Evaluating Information Essentiality
- Logic Grid Puzzle
- Logical Args
- Metaphor Boolean
- Penguins In A Table
- Presuppositions As NLI
- Reasoning About Colored Objects
- College Mathematics

## Most implemented papers

# Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs

Logical operations are performed in the embedding space by neural operators over the probabilistic embeddings.

# SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver

We demonstrate that by integrating this solver into end-to-end learning systems, we can learn the logical structure of challenging problems in a minimally supervised fashion.

# Neural Collaborative Reasoning

Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i. e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so that a user embedding can be matched with relevant item embeddings using designed or learned similarity functions.

# Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge

We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning.

# Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks

Our goal is to combine the rich multistep inference of symbolic logical reasoning with the generalization capabilities of neural networks.

# 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

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.

# Measuring Systematic Generalization in Neural Proof Generation with Transformers

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.