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【6h】Learning Distributed Event Representations with a Multi-Task Approach

機譯使用多任務方法學習分布式事件表示

【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.

【作者】Xudong Hongt;Asad Sayeed;Vera Demberg;

【作者單位】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;

【年(卷),期】2018(),

【年度】2018

【頁碼】11-21

【總頁數】11

【正文語種】eng

【中圖分類】;

【關鍵詞】

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