ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages
Table 1: With no vertical knowledge, ZSCeres-GNN achieves 65% higher recall and comparable precision in all verticals compared to the colon baseline. Even in comparison to approaches that use vertical knowledge to learn site-specific OpenIE models, ZSCeres achieves an F1 seven points higher in the University vertical.
['[BOLD] System', '[BOLD] Site-specific  [BOLD] Model', '[BOLD] Level', '[BOLD] Movie P', '[BOLD] Movie R', '[BOLD] Movie F1', '[BOLD] NBA P', '[BOLD] NBA R', '[BOLD] NBA F1', '[BOLD] University P', '[BOLD] University R', '[BOLD] University F1', '[BOLD] Average F1']
Level- I Knowledge: Across all SWDE sites (micro-averaging across all extractions), ZSCeres-GNN achieves an F1 of 0.45, in comparison with 0.43 for ZSCeres-FFNN, showing that the additional information provided by the page encoder allows for a better representation of the relationships between text fields. While this is generally a safe assumption, there are still occasional errors in the training data. In particular, we observed that the NBA vertical had considerably more ambiguous cases since most relations are numerical and the pages often contained large tables of numbers.