Contextual Commonsense Inference

Contextual commonsense inference focuses on enabling computers to understand and generate commonsense inferences within specific textual contexts, such as stories or dialogues. Current research emphasizes improving the controllability and accuracy of these inferences, often employing transformer-based language models and techniques like prompting to guide the generation process, and leveraging multiple knowledge bases for richer understanding. This area is significant because accurate contextual commonsense reasoning is crucial for developing more robust and human-like AI systems, with potential applications in natural language understanding, question answering, and dialogue systems.