  We propose an entity-centric neural cross-lingual coreference model that
builds on multi-lingual embeddings and language-independent features. We
perform both intrinsic and extrinsic evaluations of our model. In the intrinsic
evaluation, we show that our model, when trained on English and tested on
Chinese and Spanish, achieves competitive results to the models trained
directly on Chinese and Spanish respectively. In the extrinsic evaluation, we
show that our English model helps achieve superior entity linking accuracy on
Chinese and Spanish test sets than the top 2015 TAC system without using any
annotated data from Chinese or Spanish.
