论文标题
几个射击增量事件检测
Few-shot Incremental Event Detection
论文作者
论文摘要
事件检测任务可以从文本中快速检测事件，并为下游自然语言处理任务提供强大的支持。大多数这样的方法只能检测一组固定的预定义事件类。为了扩展它们以检测新类而不会失去检测旧类的能力，需要从头开始对模型进行昂贵的重新训练。增量学习可以有效地解决此问题，但需要大量的新类别数据。但是，实际上，缺乏新事件类别的高质量标记数据使得很难获得足够的数据进行模型培训。为了解决上述问题，我们定义了一项新任务，几乎没有射击的事件检测，该事件的重点是学习检测具有有限数据的新事件类，同时保留了尽可能多的范围检测旧类的能力。我们创建了一个基于少数event的少数弹射事件检测任务IFS的基准数据集，并提出了两个基准测试，即IFSED-K和IFSED-KP。实验结果表明，我们的方法比基线方法更高，并且更稳定。
Event detection tasks can enable the quick detection of events from texts and provide powerful support for downstream natural language processing tasks. Most such methods can only detect a fixed set of predefined event classes. To extend them to detect a new class without losing the ability to detect old classes requires costly retraining of the model from scratch. Incremental learning can effectively solve this problem, but it requires abundant data of new classes. In practice, however, the lack of high-quality labeled data of new event classes makes it difficult to obtain enough data for model training. To address the above mentioned issues, we define a new task, few-shot incremental event detection, which focuses on learning to detect a new event class with limited data, while retaining the ability to detect old classes to the extent possible. We created a benchmark dataset IFSED for the few-shot incremental event detection task based on FewEvent and propose two benchmarks, IFSED-K and IFSED-KP. Experimental results show that our approach has a higher F1-score than baseline methods and is more stable.
