  Event coreference resolution is an important research problem with many
applications. Despite the recent remarkable success of pretrained language
models, we argue that it is still highly beneficial to utilize symbolic
features for the task. However, as the input for coreference resolution
typically comes from upstream components in the information extraction
pipeline, the automatically extracted symbolic features can be noisy and
contain errors. Also, depending on the specific context, some features can be
more informative than others. Motivated by these observations, we propose a
novel context-dependent gated module to adaptively control the information
flows from the input symbolic features. Combined with a simple noisy training
method, our best models achieve state-of-the-art results on two datasets: ACE
2005 and KBP 2016.
