Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
Table 5: Effect of using different reconciliation schemes, context-vectors, and rewards in our RL framework (Shootings domain). The last row is the overall best scheme (deviations from this are in italics). Context refers to the type of word counts used in the state vector to represent entity context. Rewards are either per step or per episode. (S: ShooterName, K: NumKilled, W: NumWounded, C: City, Steps: Average number of steps per episode)
['[BOLD] Reconciliation  [BOLD] (RL-Extract)', '[BOLD] Context', '[BOLD] Reward', '[BOLD] Accuracy  [BOLD] S', '[BOLD] Accuracy  [BOLD] K', '[BOLD] Accuracy  [BOLD] W', '[BOLD] Accuracy  [BOLD] C', '[BOLD] Steps']
In addition to simple replacement (Replace), we also experiment with using Confidence and Majority-based reconciliation schemes for RL-Extract. We observe that the Replace scheme performs much better than the others (2-6% on all entities) and believe this is because it provides the agent with more flexibility in choosing the final values.