CAREER: Integrating denotational meaning into probabilistic language models

本项目的目的是开发概率语言模型，将指称语义信息有效地集成到识别的句法和语音阶段，用于传感器或机器人代理的口语接口。这种集成旨在允许关于代理的当前环境上下文中的单词的含义或外延的信息，以影响在做出任何识别决定之前，它分配给对其输入的假设分析的概率估计，以便接口的代理可以在其搜索中支持那些在其表示当前世界状态时“有意义”的分析。由于这些模型可以在可用于在代理的词典中建立词义的相同类型的示例上进行训练，因此预期它们将比那些仅依赖固定语料库中的单词共现统计的模型更容易适应变化的域。基于该模型的识别器最终将用作评估联网侦察机器人在搜索和救援应用中的口语接口以及移动操纵机器人在家庭护理任务中的口语接口或在与人工智能机器人和视觉实验室的其他成员的联合项目中的材料处理应用中的口语接口的试验台，以及在与明尼苏达大学空间数据库小组成员的联合项目中需要导航数字道路地图或其他地理数据的应用程序。这一模式还将被调整为教授自然语言处理概念的框架，为学生提供一个更广泛的环境，使各种处理组件可以组合在一起，并邀请学生考虑跨越传统组件之间界限的自然语言处理问题的创新解决方案。

The purpose of this project is to develop probabilistic language models that efficiently integrate denotational semantic information into syntactic and phonological stages of recognition for use in spoken language interfaces to sensor or robotic agents. This integration is intended to allow information about the meanings or denotations of words in the agent's current environmental context to influence the probability estimates it assigns to hypothesized analyses of its input, before any recognition decisions have been made, so that the interfaced agent can favor in its search those analyses that "make sense" in its representation of the current state of the world. Since these models can be trained on the same kinds of examples that may be used to establish word meanings in the agent's lexicon, it is expected that they will be easier to adapt to changing domains than those relying exclusively on word co-occurrence statistics in fixed corpora.A recognizer based on this model will eventually serve as a testbed for evaluating spoken language interfaces to networked scout robots in search and rescue applications and mobile manipulation robots in home care tasks or materials handling applications in joint projects with other members of the Artificial Intelligence Robotics and Vision Lab, as well as in applications that require navigating digital road maps or other geographic data in joint projects with members of the Spatial Databases group at the University of Minnesota. This model will also be adapted as a framework for teaching natural language processing concepts, providing students with a broader context in which various processing components may fit together, and inviting students to consider innovative solutions to natural language processing problems that cross the boundaries among traditional components.