  Entity coreference resolution is an important research problem with many
applications, including information extraction and question answering.
Coreference resolution for English has been studied extensively. However, there
is relatively little work for other languages. A problem that frequently occurs
when working with a non-English language is the scarcity of annotated training
data. To overcome this challenge, we design a simple but effective
ensemble-based framework that combines various transfer learning (TL)
techniques. We first train several models using different TL methods. Then,
during inference, we compute the unweighted average scores of the models'
predictions to extract the final set of predicted clusters. Furthermore, we
also propose a low-cost TL method that bootstraps coreference resolution models
by utilizing Wikipedia anchor texts. Leveraging the idea that the coreferential
links naturally exist between anchor texts pointing to the same article, our
method builds a sizeable distantly-supervised dataset for the target language
that consists of tens of thousands of documents. We can pre-train a model on
the pseudo-labeled dataset before finetuning it on the final target dataset.
Experimental results on two benchmark datasets, OntoNotes and SemEval, confirm
the effectiveness of our methods. Our best ensembles consistently outperform
the baseline approach of simple training by up to 7.68% in the F1 score. These
ensembles also achieve new state-of-the-art results for three languages:
Arabic, Dutch, and Spanish.
