  Various neural-based methods have been proposed so far for joint mention
detection and coreference resolution. However, existing works on coreference
resolution are mainly dependent on filtered mention representation, while other
spans are largely neglected. In this paper, we aim at increasing the
utilization rate of data and investigating whether those eliminated spans are
totally useless, or to what extent they can improve the performance of
coreference resolution. To achieve this, we propose a mention representation
refining strategy where spans highly related to mentions are well leveraged
using a pointer network for representation enhancing. Notably, we utilize an
additional loss term in this work to encourage the diversity between entity
clusters. Experimental results on the document-level CoNLL-2012 Shared Task
English dataset show that eliminated spans are indeed much effective and our
approach can achieve competitive results when compared with previous
state-of-the-art in coreference resolution.
