Table2Text systems generate textual output based on structured data utilizing machine learning. These systems are essential for fluent natural language interfaces in tools such as virtual assistants; however, left to generate freely these ML systems often produce misleading or unexpected outputs. GenNI (Generation Negotiation Interface) is an interactive visual system for high-level human-AI collaboration in producing descriptive text. The tool utilizes a deep learning model designed with explicit control states. These controls allow users to globally constrain model generations, without sacrificing the representation power of the deep learning models. The visual interface makes it possible for users to interact with AI systems following a Refine-Forecast paradigm to ensure that the generation system acts in a manner human users find suitable. We report multiple use cases on two experiments that improve over uncontrolled generation approaches, while at the same time providing fine-grained control. A demo and source code are available at https://genni.vizhub.ai .
翻译:表2Text 系统根据利用机器学习的结构性数据生成文字输出。 这些系统对于虚拟助理等工具的流利自然语言界面至关重要; 但是, 剩下来自由生成这些ML系统往往会产生误导或意外产出。 GenNI (GenNI 谈判界面) 是高级人类-AI合作制作描述性文字的互动视觉系统。 该工具使用了设计有明确控制状态的深层次学习模式。 这些控制允许用户在全球范围限制模式代代,而不会牺牲深层学习模式的体现力。 视觉界面使得用户能够按照重新配置前置模式与AI系统互动,以确保生成系统以人类用户认为合适的方式运作。 我们报告在两种实验中使用多个案例,这些实验改进了不受控制的代代方法,同时提供了精细的控制。 演示代码和源代码可在 https://genni.vizhub.ai 上查阅。