  Pattern-based labeling methods have achieved promising results in alleviating
the inevitable labeling noises of distantly supervised neural relation
extraction. However, these methods require significant expert labor to write
relation-specific patterns, which makes them too sophisticated to generalize
quickly.To ease the labor-intensive workload of pattern writing and enable the
quick generalization to new relation types, we propose a neural pattern
diagnosis framework, DIAG-NRE, that can automatically summarize and refine
high-quality relational patterns from noise data with human experts in the
loop. To demonstrate the effectiveness of DIAG-NRE, we apply it to two
real-world datasets and present both significant and interpretable improvements
over state-of-the-art methods.
