A reinforcement learning formulation to the complex question answering problem
Reinforcement learning formulation for complex question answering. Abstract summaries used for small amount of supervision using reward scores. User interaction component incorporated to guide candidate sentence selection. Experiments reveal that systems trained with user interaction perform better. The reinforcement system is able to learn automatically and effectively.
reinforcement learning complex question answering multi-document summarization reward function user interaction modeling