Affecting People with Natural Language

用英语等自然语言编写的文件越来越多地由计算机和人编写，计算机编写的文件在许多专业应用程序中经常使用，例如天气预报。不幸的是，自然语言生成的计算机模型对于个体读者的差异以及哪种表达方式最适合哪种读者的概念相对较差。因此，他们不适合更有挑战性的任务，比如说服病人戒烟，或者用幽默来改变某人对全球变暖重要性的看法。在这些任务中，文本的目标是以更深刻的方式影响读者，而不仅仅是给他们一些重要的事实。提出的研究旨在提高NLG技术对读者的灵敏度和可以实现的效果范围。为此，NLG技术专家有必要向心理学和用户建模等其他领域的研究人员学习，NLG研究人员也有必要提高该技术的鲁棒性和通用性，以应对新的挑战。

Documents written in natural languages like English are increasingly being written by computers as well as people, and computer-written documents are used routinely in many specialised applications, such as weather forecasting.Unfortunately computer models of natural language generation have a relatively poor idea about how individual readers differ and which ways of saying things will be most appropriate for which readers. As a result, they are ill-suited to more challenging tasks, such as persuading a patient to give up smoking of using humour to change someone's beliefs about the significance of globall warming. These are tasks where the goal of the text is to affect the reader in a much deeper way than just to give them some important facts.The proposed research aims to improve NLG technology in terms both of sensitivity to the reader and also of the range of effects that can be achieved. For this, it is necessary for experts in the technology of NLG to learn from researchers in other fields, for instance psychology and user modelling, and for NLG researchers in turn to increase the robustness and generality of the technology to meet the new challenges.