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Natural Language Processing in OTF Computing: Challenges and the Need for Interactive Approaches

机译:OTF计算中的自然语言处理:挑战和对交互方法的需求

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The vision of On-the-Fly (OTF) Computing is to compose and provide software services ad hoc, based on requirement descriptions in natural language. Since non-technical users write their software requirements themselves and in unrestricted natural language, deficits occur such as inaccuracy and incompleteness. These deficits are usually met by natural language processing methods, which have to face special challenges in OTF Computing because maximum automation is the goal. In this paper, we present current automatic approaches for solving inaccuracies and incompletenesses in natural language requirement descriptions and elaborate open challenges. In particular, we will discuss the necessity of domain-specific resources and show why, despite far-reaching automation, an intelligent and guided integration of end users into the compensation process is required. In this context, we present our idea of a chat bot that integrates users into the compensation process depending on the given circumstances.
机译:即时(OTF)计算的愿景是根据自然语言的需求描述来临时组成并提供软件服务。由于非技术用户使用不受限制的自然语言自己编写软件需求,因此会出现诸如准确性和不完整性之类的缺陷。这些缺陷通常可以通过自然语言处理方法来解决,因为要实现最大程度的自动化,因此在OTF计算中必须面对特殊的挑战。在本文中,我们提出了当前的自动方法来解决自然语言需求描述中的不准确性和不完整性,并阐述了公开的挑战。特别是,我们将讨论特定于域的资源的必要性,并说明为何尽管自动化程度很高,但仍需要将最终用户智能地,有指导地集成到补偿过程中。在这种情况下,我们提出了一个聊天机器人的概念,该机器人将根据给定的情况将用户整合到补偿过程中。

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