Improving Relation Extraction with Knowledge-attention
Table 1: Micro-averaged precision (P), recall (R) and F1 score on TACRED dataset. †, ‡ and †† mark the results reported in Zhang et al. (2017), Zhang et al. (2018) and Bilan and Roth (2018) respectively. ∗ marks statistically significant improvements over Self-attn with p<0.01 under one-tailed t-test.
['Model', 'P', 'R', 'F1']
It is observed that our proposed knowledge-attention encoder outperforms all CNN-based and RNN-based models by at least 1.3 F1. Meanwhile, it achieves comparable results with C-GCN and self-attention encoder, which are the current start-of-the-art single-model systems.