  The performance of relation extraction models has increased considerably with
the rise of neural networks. However, a key issue of neural relation extraction
is robustness: the models do not scale well to long sentences with multiple
entities and relations. In this work, we address this problem with an enriched
attention mechanism. Attention allows the model to focus on parts of the input
sentence that are relevant to relation extraction. We propose to enrich the
attention function with features modeling knowledge about the relation
arguments and the shortest dependency path between them. Thus, for different
relation arguments, the model can pay attention to different parts of the
sentence. Our model outperforms prior work using comparable setups on two
popular benchmarks, and our analysis confirms that it indeed scales to long
sentences with many entities.
