  Now that the performance of coreference resolvers on the simpler forms of
anaphoric reference has greatly improved, more attention is devoted to more
complex aspects of anaphora. One limitation of virtually all coreference
resolution models is the focus on single-antecedent anaphors. Plural anaphors
with multiple antecedents-so-called split-antecedent anaphors (as in John met
Mary. They went to the movies) have not been widely studied, because they are
not annotated in ONTONOTES and are relatively infrequent in other corpora. In
this paper, we introduce the first model for unrestricted resolution of
split-antecedent anaphors. We start with a strong baseline enhanced by BERT
embeddings, and show that we can substantially improve its performance by
addressing the sparsity issue. To do this, we experiment with auxiliary corpora
where split-antecedent anaphors were annotated by the crowd, and with transfer
learning models using element-of bridging references and single-antecedent
coreference as auxiliary tasks. Evaluation on the gold annotated ARRAU corpus
shows that the out best model uses a combination of three auxiliary corpora
achieved F1 scores of 70% and 43.6% when evaluated in a lenient and strict
setting, respectively, i.e., 11 and 21 percentage points gain when compared
with our baseline.
