III: Small: From Spatial Language to Spatial Data - a simulation-based approach

与空间信息相关的用户体验目前直接与数据的表示相关联；即，地理坐标精确定位地图上的位置，路线选择算法根据距离和时间确定最佳路线。相比之下，人类与世界的互动是基于经验、学习和对定性因素的推理，包括空间概念，如近/远、后、近、内。考虑到空间信息是如何在自然语言中表达的，空间表现力和可量化的空间概念之间没有唯一的映射。这在很大程度上归因于人类语言的高度语境化性质；也就是说，人类语言的解释部分是由谁和在哪里说的，以及围绕着评论的其他什么词。这个项目的挑战是如何通过破译这些空间语言术语来更好地理解人们对空间的感知。这将产生新颖的文本和音频接口，用于地理空间数据的使用，例如以人们直观的方式询问或给出方向，或用于更有效地帮助视障人士的系统。该项目的技术目标分为两个方面。第一个推力开发了一个模拟，通过让用户在虚拟环境中交互来众包地理空间语言表达数据。使用定量模型捕捉空间语言表达和交互。第二个重点是使用这些用户生成的描述来评估几种建模方法，这些方法包括(I)研究特定的城市环境以识别语境因素，以及(Ii)探索神经方法来建模语言与空间语境相适应的问题。然后，生成的模型可以用于自动将语言转换为地理空间信息，并在相反的方向上培训对话代理，这些对话代理可以使用自然、有用的地理空间语言表达来生成丰富的、上下文相关的路线和场景描述。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

User experience related to spatial information is currently directly linked to the representation of the data; that is, geographic co-ordinates pinpoint locations on maps and routing algorithms determine the best route based on distance and time. In contrast, human interaction with the world is based on experience, learning and reasoning on qualitative factors, including spatial concepts, such as near/far, behind, next to, inside. Considering how spatial information is conveyed in natural language, there is no unique mapping between the spatial expressiveness and quantifiable spatial concepts. For the most part this is attributed to the highly contextualized nature of human language; that is, what human language is interpreted in part by who and where it was said and what other words surrounded the comment. The challenge in this project is on devising means to better understand people's perception of space by deciphering such spatial language terms. This will lead to novel text and audio-based interfaces for the consumption of geospatial data such as when asking for or giving directions in a way that is intuitive to people or for systems that more effectively assist the visually impaired.The technical aims of the project are divided into two thrusts. The first thrust develops a simulation to crowdsource geospatial language expression data by having users interact in a virtual environment. The spatial language expressions and interactions are captured using quantitative models. The second thrust then uses these user-generated descriptions to evaluate several modeling approaches that include (i) studying specific urban settings to identify contextual factors and (ii) exploring neural approaches to modeling the problem of grounding language to this spatial context. The resulting models can then be used to automatically translate language to geospatial information and, in the reverse direction, to train dialogue agents that can generate enriched, contextualized route and scene descriptions with natural, useful geospatial language expressions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.