  In coreference resolution, it is important to consider all members of a
coreference cluster and decide about all of them at once. This technique can
help to avoid losing precision and also in finding long-distance relations. The
presented paper is a report of an ongoing study on an idea which proposes a new
approach for coreference resolution which can resolve all coreference mentions
to a given mention in the document in one pass. This has been accomplished by
defining an embedding method for the position of all members of a coreference
cluster in a document and resolving all of them for a given mention. In the
proposed method, the BERT model has been used for encoding the documents and a
head network designed to capture the relations between the embedded tokens.
These are then converted to the proposed span position embedding matrix which
embeds the position of all coreference mentions in the document. We tested this
idea on CoNLL 2012 dataset and although the preliminary results from this
method do not quite meet the state-of-the-art results, they are promising and
they can capture features like long-distance relations better than the other
approaches.
