Sentence-Incremental Neural Coreference Resolution
Matt Grenander Shay B. Cohen Mark Steedman
We propose a sentence-incremental neural coreference resolution system which
incrementally builds clusters after marking mention boundaries in a
shift-reduce method. The system is aimed at bridging two recent approaches at
coreference resolution: (1) state-of-the-art non-incremental models that incur
quadratic complexity in document length with high computational cost, and (2)
memory network-based models which operate incrementally but do not generalize
beyond pronouns. For comparison, we simulate an incremental setting by
constraining non-incremental systems to form partial coreference chains before
observing new sentences. In this setting, our system outperforms comparable
state-of-the-art methods by 2 F1 on OntoNotes and 7 F1 on the CODI-CRAC 2021
corpus. In a conventional coreference setup, our system achieves 76.3 F1 on
OntoNotes and 45.8 F1 on CODI-CRAC 2021, which is comparable to
state-of-the-art baselines. We also analyze variations of our system and show
that the degree of incrementality in the encoder has a surprisingly large
effect on the resulting performance.