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Found 6 papers, 0 papers with code
Logical Story Representations via FrameNet + Semantic Parsing
We propose a means of augmenting FrameNet parsers with a formal logic parser to obtain rich semantic representations of events.
Learning General Event Schemas with Episodic Logic
We present a system for learning generalized, stereotypical patterns of events—or “schemas”—from natural language stories, and applying them to make predictions about other stories.
VAL: Interactive Task Learning with GPT Dialog Parsing
By using LLMs only for specific tasks--such as predicate and argument selection--within an algorithmic framework, VAL reaps the benefits of LLMs to support interactive learning of hierarchical task knowledge from natural language.
Interactive Learning of Hierarchical Tasks from Dialog with GPT
We present a system for interpretable, symbolic, interactive task learning from dialog using a GPT model as a conversational front-end.
Mining Logical Event Schemas From Pre-Trained Language Models
We present NESL (the Neuro-Episodic Schema Learner), an event schema learning system that combines large language models, FrameNet parsing, a powerful logical representation of language, and a set of simple behavioral schemas meant to bootstrap the learning process.
Towards Natural Language Story Understanding with Rich Logical Schemas
The idea of our approach to this problem is to provide a learning system with a {``}head start{''} consisting of a semantic parser, some basic ontological knowledge, and most importantly, a small set of very general schemas about the kinds of patterns of events (often purposive, causal, or socially conventional) that even a one- or two-year-old could reasonably be presumed to possess.
