Decoupled Hyperbolic Graph Attention Network for Cross-domain Named Entity Recognition

To address the scarcity of massive labeled data, cross-domain named entity recognition (cross-domain NER) attracts increasing attention. Recent studies focus on decomposing NER into two separate tasks (i.e., entity span detection and entity type classification) to reduce the complexity of the cross-domain transfer. Despite the promising results, there still exists room for improvement. In particular, the rich domain-shared syntactic and semantic information, which are respectively important for entity span detection and entity type classification, are still underutilized. In light of these two challenges, we propose applying graph attention networks (GATs) to encode the above two kinds of information. Moreover, considering that GATs mainly operate in the Euclidean space, which may fail to capture the latent hierarchical relations among words for learning high-quality word representations, we further propose to embed words into Hyperbolic spaces. Finally, a decouple hyperbolic graph attention network (DH-GAT) is introduced for cross-domain NER. Empirical results on 10 domain pairs show that DH-GAT achieves state-of-the-art performance on several standard metrics, and further analyses are presented to better understand each component's effectiveness.