论文标题
事件核心通过多损失神经网络解决不使用参数信息
Event Coreference Resolution via a Multi-loss Neural Network without Using Argument Information
论文作者
论文摘要
事件核心分辨率（ECR）是自然语言处理（NLP）的重要任务，几乎所有现有的此任务方法都取决于事件参数信息。但是，这些方法往往会遭受事件参数提取阶段的错误传播。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困惑事件具有在真实文本中检测事件核心的模型。此外，事件的上下文信息可用于推断事件之间的核心。因此，为了减少事件参数提取传播的错误并有效地使用上下文信息，我们提出了一个多损失神经网络模型，该模型不需要任何参数信息来执行文件内部事件内部核心分辨率分辨率任务并实现与最先进的方法相比。
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.
