George Mason NLP
George Mason NLP
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human-interaction
Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing
Human-AI Interaction
We explore how machine learning systems can interact with humans effectively. This includes being able to converse with humans through dialogues, as well as proactively collaborate with and learn from humans during decision making.
Learning Structural Edits via Incremental Tree Transformations
An Imitation Game for Learning Semantic Parsers from User Interaction
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study
Reinforced Dynamic Reasoning for Conversational Question Generation
IEC: Towards Interest-Eliciting Neural Conversational Agents
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