Learning Dynamic Context Augmentation for Global Entity Linking
Table 5: Study on Different Attention Mechanisms on the Dynamic Context. Results (using ETHZ-Attn as local model) show that different attention mechanisms have similar impact on the performance.
['[BOLD] System', '[BOLD] In-KB acc. (%)  [BOLD] SL', '[BOLD] In-KB acc. (%)  [BOLD] RL']
3. Study of different attention mechanisms. Average Sum treats all previously linked entities equally with a uniform distribution. Soft Attention skips the pruning step for entities with low weight scores. Soft&Hard Attention stands for the strategy used in our model. It is obvious that the attention mechanism does show positive influence on the linking performance compared with Average Sum. Hard pruning brings slight further improvement.