Parallel Data Helps Neural Entity Coreference Resolution
Gongbo Tang Christian Hardmeier
Coreference resolution is the task of finding expressions that refer to the
same entity in a text. Coreference models are generally trained on monolingual
annotated data but annotating coreference is expensive and challenging.
Hardmeier et al.(2013) have shown that parallel data contains latent anaphoric
knowledge, but it has not been explored in end-to-end neural models yet. In
this paper, we propose a simple yet effective model to exploit coreference
knowledge from parallel data. In addition to the conventional modules learning
coreference from annotations, we introduce an unsupervised module to capture
cross-lingual coreference knowledge. Our proposed cross-lingual model achieves
consistent improvements, up to 1.74 percentage points, on the OntoNotes 5.0
English dataset using 9 different synthetic parallel datasets. These
experimental results confirm that parallel data can provide additional
coreference knowledge which is beneficial to coreference resolution tasks.