A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution
This work addresses event coreference resolution for information extraction applications, presenting an incremental improvement over existing methods.
The paper tackles the problem of noisy symbolic features in event coreference resolution by proposing a context-dependent gated module to adaptively control information flow, achieving state-of-the-art results on ACE 2005 and KBP 2016 datasets.
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.