Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

Understand

Most successful information extraction systems operate with access to a large collection of documents.

• In this work, we explore the task of acquiring and incorporating external evidence to improve extraction accuracy in domains where the amount of training data is scarce.
• This process entails issuing search queries, extraction from new sources and reconciliation of extracted values, which are repeated until sufficient evidence is collected.
• We approach the problem using a reinforcement learning framework where our model learns to select optimal actions based on contextual information.