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Found 9 papers, 6 papers with code
A Mathematical Framework, a Taxonomy of Modeling Paradigms, and a Suite of Learning Techniques for Neural-Symbolic Systems
There is a pressing need for a unifying theory to illuminate the commonalities and differences in approaches and enable further progress.
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic
Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i. e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog.
Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning
We leverage convex and bilevel optimization techniques to develop a general gradient-based parameter learning framework for neural-symbolic (NeSy) systems.
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation
Such object navigation tasks usually require large-scale training in visual environments with labeled objects, which generalizes poorly to novel objects in unknown environments.
CausalDialogue: Modeling Utterance-level Causality in Conversations
Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans.
Emotion Recognition in Conversation using Probabilistic Soft Logic
Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community.
NeuPSL: Neural Probabilistic Soft Logic
In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks.
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.
Local Explanation of Dialogue Response Generation
To gain insights into the reasoning process of a generation model, we propose a new method, local explanation of response generation (LERG) that regards the explanations as the mutual interaction of segments in input and output sentences.
