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Event coreference resolution is an important research problem with manyapplications. Despite the recent remarkable success of pretrained languagemodels, we argue that it is still highly beneficial to utilize symbolicfeatures for the task. However, as the input for coreference resolutiontypically comes from upstream components in the information extractionpipeline, the automatically extracted symbolic features can be noisy andcontain errors. Also, depending on the specific context, some features can bemore informative than others. Motivated by these observations, we propose anovel context-dependent gated module to adaptively control the informationflows from the input symbolic features. Combined with a simple noisy trainingmethod, our best models achieve state-of-the-art results on two datasets: ACE2005 and KBP 2016.

A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution