Learning what to read: Focused machine reading
Table 2: Ablation test on the features used to represent the RL state.
['[EMPTY]', 'All', '− Iteration', '− Query', '− Same', '− Ranks (f4)', '− Particip.']
We performed an ablation test on the features that encode the RL state. Similar to Section 5, we grouped the features into five different groups, and we measured the impact of removing one feature group at a time. Overall, the amount of paths found doesn’t have a significant amount of variance, but the efficiency of the search (amount of papers read and number of queries made) depends on several feature groups. For example, features (f1), (f2), and (f4) have a large effect on both the number of papers read and the number of queries made. Removing the feature (f5) actually reduces the number of papers read by approximately 2K with a minimal reduction in the number of paths found, which suggests that this task could benefit from feature selection.