Learning Distributed Event Representations with a Multi-Task Approach

机译：使用多任务方法学习分布式事件表示

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摘要

Human world knowledge contains information about prototypical events and their participants and locations. In this paper, we train the first models using multi-task learning that can both predict missing event participants and also perform semantic role classification based on semantic plausibility. Our best-performing model is an improvement over the previous state-of-the-art on thematic fit modelling tasks. The event embeddings learned by the model can additionally be used effectively in an event similarity task, also outperforming the state-of-the-art.

机译：人类世界知识包含有关原型事件及其参与者和位置的信息。在本文中，我们使用多任务学习训练了第一个模型，该模型既可以预测丢失的事件参与者，也可以基于语义合理性执行语义角色分类。我们表现最好的模型是对先前主题拟合模型任务的最新改进。通过模型学习到的事件嵌入可以有效地用于事件相似性任务中，并且性能也超过了现有技术。

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来源
《Seventh joint conference on lexical and computational semantics》|2018年|11-21|共11页
会议地点 New Orleans(US)
作者
Xudong Hongt; Asad Sayeed; Vera Demberg;
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作者单位

Dept. of Language Science and Technology, Saarland University;

Dept. of Philosophy, Linguistics, and Theory of Science, University of Gothenburg;

Dept. of Language Science and Technology, Saarland University;

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入库时间 2022-08-26 13:45:45

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Learning Distributed Event Representations with a Multi-Task Approach

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