论文标题
利用低数据设置中的粗粒数据进行事件提取
Utilizing coarse-grained data in low-data settings for event extraction
论文作者
论文摘要
注释事件信息提取系统的文本数据很难，昂贵且容易出错。我们研究了整合粗粒数据（文档或句子标签）的可行性，这些数据更可行，而不是注释更多文档。除了代币分类的主要任务外，我们还使用了具有两个辅助任务的多任务模型，即文档和句子二进制分类。我们对上述集成进行了一系列的实验。结果表明，尽管引入额外的粗粒数据可提供更大的改进和鲁棒性，但仅增加对任何事件信息的负面文档，仍然可以增加增益。
Annotating text data for event information extraction systems is hard, expensive, and error-prone. We investigate the feasibility of integrating coarse-grained data (document or sentence labels), which is far more feasible to obtain, instead of annotating more documents. We utilize a multi-task model with two auxiliary tasks, document and sentence binary classification, in addition to the main task of token classification. We perform a series of experiments with varying data regimes for the aforementioned integration. Results show that while introducing extra coarse-grained data offers greater improvement and robustness, a gain is still possible with only the addition of negative documents that have no information on any event.
