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Found 27 papers, 10 papers with code
Zero-shot Entity Linking with Less Data
Interestingly, we discovered that BLINK exhibits diminishing returns, i. e., it reaches 98% of its performance with just 1% of the training data and the remaining 99% of the data yields only a marginal increase of 2% in the performance.
A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making
To enable decision-making under uncertainty and partial observability, we developed a novel probabilistic neuro-symbolic framework, Probabilistic Logical Neural Networks (PLNN), which combines the capabilities of logical reasoning with probabilistic graphical models.
Compositional Program Generation for Few-Shot Systematic Generalization
Compositional generalization is a key ability of humans that enables us to learn new concepts from only a handful examples.
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning
Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games.
MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types
In this work, we propose a new evaluation scheme to model human judgments in 7 NLP tasks, based on the fine-grained mismatches between a pair of texts.
Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency
We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-supervised method for training a neural semantic parser.
A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases
Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata.
Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks
Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data.
LOA: Logical Optimal Actions for Text-based Interaction Games
We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games.
Neuro-Symbolic Reinforcement Learning with First-Order Logic
Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided.
SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases
In addition, to demonstrate extensi-bility to additional reasoning types we evaluate on multi-hopreasoning datasets and a new Temporal KBQA benchmarkdataset on Wikidata, namedTempQA-WD1, introduced in thispaper.
Logical Credal Networks
This paper introduces Logical Credal Networks, an expressive probabilistic logic that generalizes many prior models that combine logic and probability.
Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion
Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings.
A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering
Relation linking is a crucial component of Knowledge Base Question Answering systems.
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversational systems.
Reinforcement Learning with External Knowledge by using Logical Neural Networks
Specifically, we propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide.
Logic Embeddings for Complex Query Answering
Answering logical queries over incomplete knowledge bases is challenging because: 1) it calls for implicit link prediction, and 2) brute force answering of existential first-order logic queries is exponential in the number of existential variables.
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
Knowledgebase question answering systems are heavily dependent on relation extraction and linking modules.
Ranked #1 on
Relation Linking
on QALD-7
Foundations of Reasoning with Uncertainty via Real-valued Logics
Our class of sentences are very rich, and each describes a set of possible real values for a collection of formulas of the real-valued logic, including which combinations of real values are possible.
Logical Neural Networks
We propose a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning).
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates
Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow.
How can AI Automate End-to-End Data Science?
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it.
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways.
An ADMM Based Framework for AutoML Pipeline Configuration
We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines.
Which Space Partitioning Tree to Use for Search?
We consider the task of nearest-neighbor search with the class of binary-space-partitioning trees, which includes kd-trees, principal axis trees and random projection trees, and try to rigorously answer the question which tree to use for nearest-neighbor search?''
Local Support Vector Machines:Formulation and Analysis
We provide a formulation for Local Support Vector Machines (LSVMs) that generalizes previous formulations, and brings out the explicit connections to local polynomial learning used in nonparametric estimation literature.
