论文标题
双向迭代及时调整事件参数提取
Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction
论文作者
论文摘要
最近，及时的调整引起了人们对事件参数提取（EAE）的不断增长的兴趣。但是，由于缺乏对实体信息的考虑，现有的及时调整方法尚未实现令人满意的性能。在本文中，我们为EAE提出了一种双向迭代的及时调整方法，其中EAE任务被视为一项紧固的任务，以充分利用实体信息和预训练的语言模型（PLMS）。此外，我们的方法通过将上下文实体的参数角色引入及时的构造来探讨事件参数交互。由于模板和口头化合物是披风式提示中的两个至关重要的组件，因此我们建议利用角色标签语义知识来构建语义语言语言，并为EAE任务设计三种模板。具有标准和低资源设置的ACE 2005英语数据集的实验表明，所提出的方法明显优于同行最新方法。我们的代码可在https://github.com/hustminslab/bip上找到。
Recently, prompt-tuning has attracted growing interests in event argument extraction (EAE). However, the existing prompt-tuning methods have not achieved satisfactory performance due to the lack of consideration of entity information. In this paper, we propose a bi-directional iterative prompt-tuning method for EAE, where the EAE task is treated as a cloze-style task to take full advantage of entity information and pre-trained language models (PLMs). Furthermore, our method explores event argument interactions by introducing the argument roles of contextual entities into prompt construction. Since template and verbalizer are two crucial components in a cloze-style prompt, we propose to utilize the role label semantic knowledge to construct a semantic verbalizer and design three kinds of templates for the EAE task. Experiments on the ACE 2005 English dataset with standard and low-resource settings show that the proposed method significantly outperforms the peer state-of-the-art methods. Our code is available at https://github.com/HustMinsLab/BIP.
