Improving Event Detection by Exploiting Label Hierarchy

Event types are hierarchical, yet most existing methods for event detection classify candidate triggers into fine-grained event types directly, without considering the rich semantic correlations in the hierarchy of event types. To fully utilize such information to improve the detection of fine-grained event types, we propose a three-layer label hierarchy and introduce the detection of two coarser-grained types as auxiliary classification tasks. In particular, we leverage the supplementary supervision information from label hierarchy by a novel Logits Mapping (LM) strategy, which generates logits (the intermediate representations fed into classifier) for coarser-grained types by heuristic mapping of logits for fine-grained types. In this way, training signals provided by auxiliary tasks can help the encoder produce more precise logits via back propagation, thus providing a simple (no extra parameter needed) yet effective way to improve the target task. Results of extensive experiments on the ACE 2005 show that LM can not only be easily integrated into the state-of-the-art methods and achieve significant improvement over them, but also can effectively alleviate the data sparseness problem.