1. Extending Event Detection to New Types with Learning from Keywords
EMNLP會議，作者為Viet Dac Lai and Thien Huu Nguyen
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.
傳統的事件檢測是根據一組預定義的事件類型對給定句子中的單詞或短語進行分類。這種預定義集合的局限性在於，它阻止了事件檢測模型適應新的事件類型。我們研究了一種新的事件檢測方法，它通過幾個關鍵字來描述類型，以匹配文檔中的上下文。這有助於將模型操作為新類型。我們在新的公式中引入了一種新的基於特征的卷積神經網絡事件檢測注意機制。我們的大量實驗證明了新的事件檢測擴展公式的優點，以及針對該問題提出的注意機制。
2. Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection
AAAI會議，作者為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.
語言表達的模糊性給事件檢測帶來了很大的挑戰。為了消除事件類型的歧義，當前的方法依賴於外部NLP工具包來構建知識表示。不幸的是，這些方法在流水線模式中工作，並且遭受錯誤傳播問題的困擾。本文首次提出了一種基於對抗性模仿的知識提取方法，以解決從句子中獲取知識用於事件檢測的難題在我們的方法中，首先設計了一個教師模塊，從基礎真理注釋中學習知識表示。然后，我們建立了一個學生模塊，它只接受原始句子作為輸入。學生模塊在對抗性鑒別器的指導下學習模仿教師的行為。通過這種方式，從rawstence中提取知識的過程被隱式地集成到學生模塊的特征編碼階段。最后，增強的student用於事件檢測，它處理原始文本，不需要額外的工具包，自然消除了管道方法面臨的錯誤傳播問題。我們在ACE 2005數據集上進行了大量的實驗，實驗結果證明了我們的方法的有效性。
3. Event Detection without Triggers
NAACL會議，作者為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.
事件檢測（ED）的目標是檢測事件的發生並對其進行分類。以前的工作通過識別和分類事件觸發器來解決這個問題，事件觸發器被定義為最清楚地表示事件發生的單詞或短語。因此，現有的方法在訓練數據中需要注釋的觸發器和事件類型。然而，觸發器對事件檢測並不重要，注釋者從一個給定的句子中，特別是從一個長句子中，挑選出“最清晰”的單詞是非常耗時的。訓練語料庫昂貴的注釋限制了現有方法的應用。為了減少人工操作，我們探索不使用觸發器檢測事件。在這項工作中，我們提出了一個新的框架，稱為帶有注意機制的類型感知偏向神經網絡（TBNNAM），它根據目標事件類型對句子的表示進行編碼。實驗結果證明了該方法的有效性。值得注意的是，與使用注釋觸發器的最新技術相比，該方法甚至獲得了競爭性的性能。
4. Joint Event Extraction Based on Hierarchical Event Schemas From FrameNet
IEEE會議，作者為WEI LI, DEZHI CHENG，LEI HE, YUANZHUO WANG, AND XIAOLONG JIN
Event extraction is useful for many practical applications, such as news summarization and information retrieval. However, the popular automatic context extraction (ACE) event extraction program only defines very limited and coarse event schemas, which may not be suitable for practical applications. FrameNet is a linguistic corpus that defines complete semantic frames and frame-to-frame relations. As frames in FrameNet share highly similar structures with event schemas in ACE and many frames actually express events, we propose to redefine the event schemas based on FrameNet. Specifically, we extract frames expressing event information from FrameNet and leverage the frame-to-frame relations to build a hierarchy of event schemas that are more fine-grained and have much wider coverage than ACE. Based on the new event schemas, we propose a joint event extraction approach that leverages the hierarchical structure of event schemas and frame-to-frame relations in FrameNet. The extensive experiments have verified the advantages of our hierarchical event schemas and the effectiveness of our event extraction model. We further apply the results of our event extraction model on news summarization. The results show that the summarization approach based on our event extraction model achieves significant better performance than several state-ofthe-art summarization approaches, which also demonstrates that the hierarchical event schemas and event extraction model are promising to be used in the practical applications.
事件抽取在新聞摘要、信息檢索等實際應用中具有重要的意義。然而，目前流行的自動上下文抽取（ACE）事件抽取程序只定義了非常有限且粗糙的事件模式，可能不適合實際應用。框架網是一個定義完整語義框架和框架間關系的語言語料庫。由於幀網絡中的幀與ACE中的事件模式具有高度相似的結構，並且許多幀實際上表示事件，因此我們建議基於幀網絡重新定義事件模式。具體來說，我們從FrameNet中提取表示事件信息的幀，並利用幀到幀的關系來構建比ACE更細粒度、覆蓋范圍更廣的事件模式層次結構。在新事件模式的基礎上，我們提出了一種利用事件模式的層次結構和框架到框架關系的聯合事件抽取方法。大量的實驗證明了層次事件模式的優越性和事件抽取模型的有效性。我們進一步將事件抽取模型的結果應用到新聞摘要中。結果表明，基於事件抽取模型的摘要方法比目前幾種最新的摘要方法具有更好的性能，這也說明了層次事件模式和事件抽取模型在實際應用中具有廣闊的應用前景。