Building on the baseline above, we establish a new architecture that is able to capture the sub-event types as well as their duration.
we (i) establish a neural baseline that outperforms a graph-based state-of-the-art method for binary sub-event detection (2.7% micro-F 1 improvement), as well as (ii) demonstrate superiority of a recurrent neural network model on the posts sequence level for labeled sub-events (2.4% bin-level F 1 improvement over non-sequential models).