Represent, Aggregate, and Constrain: A Novel Architecture for Machine Reading from Noisy Sources
Table 1: Event extraction results across different systems on the Stanford Plane Crash Dataset.
['[EMPTY]', 'Aggregation', 'Score  [BOLD] P', 'Score  [BOLD] R', 'Score  [BOLD] F1', 'Nulls  [BOLD] P', 'Nulls  [BOLD] R', 'Nulls  [BOLD] F1', 'MRR']
In comparison to previous work, our any configuration of our RAC architecture with sum-based aggregation outperforms the best existing systems by a minimum of 9.8 F1. In comparison to the various Mention-CNN systems, it is clear that this improvement is not a result of different features or the use of pre-trained word embeddings, or even the representational power of the CNN-based embeddings. Rather, it is attributable to training end-to-end with aggregation and a cluster-level loss function.