Parallel Data Helps Neural Entity Coreference Resolution
This work addresses coreference resolution for NLP researchers by introducing a cross-lingual module, but it is incremental as it builds on prior findings about parallel data.
The paper tackled the problem of expensive coreference annotation by proposing a model that exploits latent coreference knowledge from parallel data, achieving improvements of up to 1.74 percentage points on the OntoNotes 5.0 English dataset.
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.