1. Extending Event Detection to New Types with Learning from Keywords
EMNLP会议，作者为Viet Dac Lai and Thien Huu Nguyen
摘要
Traditional event detection classifies a word or a phrase in a given sentence for a set of predefined event types. The limitation of such predefined set is that it prevents the adaptation of the event detection models to new event types. We study a novel formulation of event detection that describes types via several keywords to match the contexts in documents. This facilitates the operation of the models to new types. We introduce a novel feature-based attention mechanism for convolutional neural networks for event detection in the new formulation. Our extensive experiments demonstrate the benefits of the new formulation for new type extension for event detection as well as the proposed attention mechanism for this problem.
传统的事件检测是根据一组预定义的事件类型对给定句子中的单词或短语进行分类。这种预定义集合的局限性在于，它阻止了事件检测模型适应新的事件类型。我们研究了一种新的事件检测方法，它通过几个关键字来描述类型，以匹配文档中的上下文。这有助于将模型操作为新类型。我们在新的公式中引入了一种新的基于特征的卷积神经网络事件检测注意机制。我们的大量实验证明了新的事件检测扩展公式的优点，以及针对该问题提出的注意机制。
2. Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection
AAAI会议，作者为Jian Liu， Yubo Chen, Kang Liu
The ambiguity in language expressions poses a great challenge for event detection. To disambiguate event types, current approaches rely on external NLP toolkits to build knowledge representations. Unfortunately, these approaches work in a pipeline paradigm and suffer from error propagation problem. In this paper, we propose an adversarial imitation based knowledge distillation approach, for the first time, to tackle the challenge of acquiring knowledge from rawsentences for event detection. In our approach, a teacher module is first devised to learn the knowledge representations from the ground-truth annotations. Then, we set up a student module that only takes the raw-sentences as the input. The student module is taught to imitate the behavior of the teacher under the guidance of an adversarial discriminator. By this way, the process of knowledge distillation from rawsentence has been implicitly integrated into the feature encoding stage of the student module. To the end, the enhanced student is used for event detection, which processes raw texts and requires no extra toolkits, naturally eliminating the error propagation problem faced by pipeline approaches. We conduct extensive experiments on the ACE 2005 datasets, and the experimental results justify the effectiveness of our approach.
语言表达的模糊性给事件检测带来了很大的挑战。为了消除事件类型的歧义，当前的方法依赖于外部NLP工具包来构建知识表示。不幸的是，这些方法在流水线模式中工作，并且遭受错误传播问题的困扰。本文首次提出了一种基于对抗性模仿的知识提取方法，以解决从句子中获取知识用于事件检测的难题在我们的方法中，首先设计了一个教师模块，从基础真理注释中学习知识表示。然后，我们建立了一个学生模块，它只接受原始句子作为输入。学生模块在对抗性鉴别器的指导下学习模仿教师的行为。通过这种方式，从rawstence中提取知识的过程被隐式地集成到学生模块的特征编码阶段。最后，增强的student用于事件检测，它处理原始文本，不需要额外的工具包，自然消除了管道方法面临的错误传播问题。我们在ACE 2005数据集上进行了大量的实验，实验结果证明了我们的方法的有效性。
3. Event Detection without Triggers
NAACL会议，作者为Shulin Liu, Yang Li, Xinpeng Zhou, Tao Yang, Feng Zhang
The goal of event detection (ED) is to detect the occurrences of events and categorize them. Previous work solved this task by recognizing and classifying event triggers, which is defined as the word or phrase that most clearly expresses an event occurrence. As a consequence, existing approaches required both annotated triggers and event types in training data. However, triggers are nonessential to event detection, and it is time-consuming for annotators to pick out the “most clearly” word from a given sentence, especially from a long sentence. The expensive annotation of training corpus limits the application of existing approaches. To reduce manual effort, we explore detecting events without triggers. In this work, we propose a novel framework dubbed as Type-aware Bias Neural Network with Attention Mechanisms (TBNNAM), which encodes the representation of a sentence based on target event types. Experimental results demonstrate the effectiveness. Remarkably, the proposed approach even achieves competitive performances compared with state-of-the-arts that used annotated triggers.
事件检测（ED）的目标是检测事件的发生并对其进行分类。以前的工作通过识别和分类事件触发器来解决这个问题，事件触发器被定义为最清楚地表示事件发生的单词或短语。因此，现有的方法在训练数据中需要注释的触发器和事件类型。然而，触发器对事件检测并不重要，注释者从一个给定的句子中，特别是从一个长句子中，挑选出“最清晰”的单词是非常耗时的。训练语料库昂贵的注释限制了现有方法的应用。为了减少人工操作，我们探索不使用触发器检测事件。在这项工作中，我们提出了一个新的框架，称为带有注意机制的类型感知偏向神经网络（TBNNAM），它根据目标事件类型对句子的表示进行编码。实验结果证明了该方法的有效性。值得注意的是，与使用注释触发器的最新技术相比，该方法甚至获得了竞争性的性能。