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Not Everybody's Special: Using Neighbors in Referring Expressions with Uncertain Attributes

机译:并非每个人都特别:使用邻居在引用具有不确定属性的表达式时

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Referring expression generation is widely considered a basic building block of any natural language generation system. Generating these phrases, which can point out a single object from a group of objects, has been studied extensively in that community. However, to build systems which can discuss images in an intelligent way, it is necessary to consider additional factors unique to the visual domain. In this paper we consider the use of neighbors as anchors to create a referring expression for a person in a group image. We describe a target person using the people around him, when we cannot find a reliable set of attributes to describe the target himself. We first present a method for including neighbors in a referring expression, and discuss several ways of presenting this data to a user. We show through experiments that using descriptions with neighbors can significantly improve the probability of conveying the correct information to a user.
机译:引用表达生成被广泛认为是任何自然语言生成系统的基本构建块。在这些社区中,已经广泛研究了生成可以指示一组对象中的单个对象的这些短语。但是,要构建可以以智能方式讨论图像的系统,必须考虑视觉域特有的其他因素。在本文中,我们考虑使用邻居作为锚来为组图像中的人创建参照表达。当我们无法找到可靠的属性集来描述目标人时,我们使用周围的人来描述目标人。我们首先介绍一种在引用表达式中包含邻居的方法,并讨论将数据呈现给用户的几种方法。我们通过实验表明,与邻居一起使用描述可以显着提高向用户传达正确信息的可能性。

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