Event co-reference resolution is an important task in natural language processing, and nearly all the existing approaches for this task rely on event argument information. However, these methods tend to suffer from error propagation from event argument extraction. Additionally, not every event mention contains all arguments of an event, and the argument information may confuse the model where events contain arguments to detect an event co-reference in real text. Furthermore, the context information of an event is useful to infer the co-reference between events. Thus, to reduce the errors propagated from event argument extraction and use context information effectively, we propose a multi-loss neural network model that does not require any argument information relating to the within-document event co-reference resolution task; furthermore, it achieves a significantly better performance than the state-of-the-art methods.