Event coreference resolution(ECR) is an important task in Natural Language Processing (NLP) and nearly all the existing approaches to this task rely on event argument information. However, these methods tend to suffer from error propagation from the stage of event argument extraction. Besides, not every event mention contains all arguments of an event, and argument information may confuse the model that events have arguments to detect event coreference in real text. Furthermore, the context information of an event is useful to infer the coreference between events. Thus, in order 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 need any argument information to do the within-document event coreference resolution task and achieve a significant performance than the state-of-the-art methods.

中文翻译：

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在不使用参数信息的情况下通过多损失神经网络解决事件共指

事件共指解析（ECR）是自然语言处理（NLP）中的一项重要任务，几乎所有现有方法都依赖于事件参数信息。然而，这些方法往往会受到事件参数提取阶段的错误传播的影响。此外，并非每个事件提及都包含事件的所有参数，并且参数信息可能会混淆事件具有参数以检测真实文本中的事件共指的模型。此外，事件的上下文信息有助于推断事件之间的共指。因此，为了减少从事件参数提取中传播的错误并有效地使用上下文信息，