Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
Table 2: Performance comparison on the GDA development and test sets.
['Model', 'Dev | Test F1 (%) Overall', 'Dev | Test F1 (%) Overall', 'Dev | Test F1 (%) Intra', 'Dev | Test F1 (%) Intra', 'Dev | Test F1 (%) Inter', 'Dev | Test F1 (%) Inter']
We also apply our model on the distantly supervised GDA dataset. This indicates that document-level information is helpful. However, performance differs for inter-sentence pairs and in particular for the fully connected graph (Full) baseline. We partially attribute this behavior to the small number of inter-sentence pairs in the GDA dataset (only 13% compared to 30% in the CDR dataset) that results in inadequate learning patters for EoG. We leave further investigation as part of future work.