Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction
Table 2: P@N of Weston and variants of our proposed framework.
['P@N(%)', '10%', '30%', '50%']
As one can see, Hrere-base significantly outperforms previous state-of-the-art neural models and Weston over the entire range of recall. This suggests that introducing knowledge representation consistently results in improvements, which validates our motivating hypothesis. Hrere-naive simply optimizes both local and global loss at the same time without attempting to connect them. We can see that Hrere-full is not only consistently superior but also more stable than Hrere-naive when the recall is less than 0.1. One possible reason for the instability is that the results may be dominated by one of the representations and biased toward it. This suggests that (1) jointly learning the heterogeneous representations bring mutual benefits which are out of reach of previous methods that learn each independently; (2) connecting heterogeneous representations can increase the robustness of the framework.