Through ablation analysis, we observe that the memory buffer and the KG features contribute significantly to this performance gain.
by forming semantic abstractions for each highlighted text span, we decompose a single complex tagging task in a large label space into correlated but simpler sub-tasks, which are likely to generalize better when the training data is limited.