Position-aware Self-attention with Relative Positional Encodings for Slot Filling
Table 2: Comparison of ensemble models evaluated on TACRED test set, micro-averaged over instances.
['[BOLD] Ensemble Models', '[BOLD] P', '[BOLD] R', '[BOLD] F1']
In addition to testing the single model results, we also follow the same ensembling strategy applied by \newciteD17-1004, where five models are trained with a different random seed and later on, using ensemble majority vote a relation class is selected for each sample. Our ensembled model reaches a slightly higher F1-score as that of \newciteD17-1004, namely, 67.3%. However, there are significant differences regarding precision and recall. Their ensembled model achieves a relatively high precision of 70.1%, while our model reaches high recall of 69.7%.