Towards Generalized Natural Language Generation with Distributional Semantics

从智能手机到智能汽车、机器人助手等，许多新兴技术的共同目标是提取关于世界的有用信息，以便让用户更好地控制周围环境。自然语言处理领域提供了从文本中提取意义、与其进行推理并最终产生文本和语音以便将信息传递回用户的技术。 最近在个人助理应用和新闻摘要系统方面的成功提高了对能够交互地向用户产生反馈的自然语言生成(NLG)系统的需求。然而，许多现有的方法只适用于高度受限的领域和任务，典型的例子是带有预编程模板的GPS导航系统，用于生成驾驶方向。 我的研究计划旨在为NLG生成一个通用的计算帐户，该帐户对应用程序的主题、语言类型和目标的差异非常敏感。这将需要分析NLG系统的参数空间，以表征其多样性，以及相应的自适应和可表达的语义，以支持NLG所需的推理和推理。 对于前一个要求，我的研究小组将为NLG开发技术，考虑到该系统在其更广泛的背景下的预期用途。例如，新闻文本摘要系统可能会强调简洁和正式等因素，而互动教育游戏可能会强调其他因素，如简洁性和趣味性。 对于后一种需求，我的研究程序将研究分布语义(DS)，这是一种数据驱动的意义建模方法，无需人工注释即可进行训练。我的研究将专注于使用DS对魁北克或Android等实体以及2016年奥运会等事件的含义进行建模，这是推理NLG应该表达的重要信息所必需的。 这项研究的影响将是将NLG从局限于高度受限和特定领域的场景，转变为以自然和普遍的方式与交互系统集成。由该研究计划贡献的工具、技术和框架将证明，由表现力、数据驱动的语义支持的NLG系统可以为教育、娱乐或商业而开发和定制。

From smartphones to smart cars, robotic assistants and beyond, the common goal of many emerging technologies is to extract useful information about the world in order to give users greater control over their surroundings. The field of natural language processing provides techniques to extract meaning from text, reason with it, and finally produce text and speech in order to relay information back to the user. Recent successes in personal assistant applications and news summarization systems have raised the demand for natural language generation (NLG) systems that can interactively produce feedback to the user. Yet many existing methods only work well on highly restricted domains and tasks, as typified by a GPS navigation system with pre-programmed templates for generating driving directions. My research program aims to produce a generalized computational account of NLG that is sensitive to differences in the topics, language types, and goals of an application. This will require an analysis of the parameter space of NLG systems in order to characterize their diversity, as well as a correspondingly adaptive and expressive semantics that can support the reasoning and inferences that are necessary for NLG. For the former requirement, my research group will develop techniques for NLG that take into account the desired usage of the system in its broader context. For example, a news text summarization system might emphasize factors such as brevity and formality, whereas an interactive educational game might emphasize other factors such as simplicity and interest. For the latter requirement, my research program will investigate distributional semantics (DS), a data-driven approach to modelling meaning that can be trained without human annotation effort. My research will focus on using DS to model the meaning of entities such as Quebec or Android, as well as events such as the Olympic Games of 2016, which is required in order to reason about the important information that should be expressed by NLG. The impact of this research will be to move NLG from being confined to highly restricted and domain-specific scenarios to being integrated with interactive systems in a natural and pervasive manner. The tools, techniques, and framework contributed by this research program will demonstrate that NLG systems that are supported by expressive, data-driven semantics can be developed and tailored for education, entertainment, or business.