Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
Table 5: Ablation analysis of edge enhancements on the CDR development set.
['Model', 'F1 (%) Overall', 'F1 (%) Intra', 'F1 (%) Inter']
In general, intra-sentence pairs are not affected by these settings. However, for inter-sentence pairs, removal of node type embeddings and distance embeddings results in a 2% and 5% drop in terms of F1-score. These results indicate that the interactions between different elements in a document, along with the distance between sentences and mentions, play an important role in inter-sentence pair inference. Removing all of these settings does not perform worse than removing one of them, which might indicate model overfitting. We plan to further investigate this as part of future work.