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arXiv 2112.03073cs.CLcs.AI
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基于记忆损失预测模型的事件抽取主动学习

Active Learning for Event Extraction with Memory-based Loss Prediction Model

Southeast University（东南大学）

Shirong Shen, Zhen Li, Guilin Qi

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摘要：

事件提取（EE）在许多工业应用场景中扮演着重要的角色，高质量的EE方法需要大量的人工标注数据来训练监督学习模型。然而，获取标注数据的成本非常高，尤其是对于领域事件的标注，需要相应领域的专家参与。因此，我们引入了主动学习（AL）技术，以减少事件标注的成本。但是现有的人工智能方法存在两个主要问题，使得它们不能很好地用于事件抽取。首先，现有的基于池的选择策略在计算成本和样本有效性方面存在局限性。其次，现有的样本重要性评价缺乏对局部样本信息的利用。在本文中，我们提出了一种新的深度AL方法EE。我们提出了一个基于批量的选择策略和基于记忆的损失预测模型（MBLP），以有效地选择未标记的样本。在选择过程中，我们使用内部-外部样本损失排序方法来评估样本的重要性，利用本地信息。最后，我们提出了一种延迟训练策略来训练MBLP模型。在三个域数据集上进行了大量的实验，我们的方法优于其他最先进的方法。

英文摘要：

Event extraction (EE) plays an important role in many industrial application scenarios, and high-quality EE methods require a large amount of manual annotation data to train supervised learning models. However, the cost of obtaining annotation data is very high, especially for annotation of domain events, which requires the participation of experts from corresponding domain. So we introduce active learning (AL) technology to reduce the cost of event annotation. But the existing AL methods have two main problems, which make them not well used for event extraction. Firstly, the existing pool-based selection strategies have limitations in terms of computational cost and sample validity. Secondly, the existing evaluation of sample importance lacks the use of local sample information. In this paper, we present a novel deep AL method for EE. We propose a batch-based selection strategy and a Memory-Based Loss Prediction model (MBLP) to select unlabeled samples efficiently. During the selection process, we use an internal-external sample loss ranking method to evaluate the sample importance by using local information. Finally, we propose a delayed training strategy to train the MBLP model. Extensive experiments are performed on three domain datasets, and our method outperforms other state-of-the-art methods.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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