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

III：小：从空间语言到空间数据 - 基于模拟的方法

• 批准号: 2127901
• 负责人: Dieter Pfoser
• 金额: $ 48.91万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2127901&HistoricalAwards=false
• 关键词: III; Small; Spatial; Language; Data

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.

目前，与空间信息相关的用户体验与数据的表示直接链接；也就是说，地理坐标在地图上查明位置，路由算法确定了基于距离和时间的最佳路线。相比之下，人类与世界的互动是基于经验，学习和推理定性因素，包括空间概念，例如近/远处，后面，旁边，内部。考虑到如何以自然语言传达空间信息，空间表现力和可量化的空间概念之间没有独特的映射。在大多数情况下，这归因于人类语言高度上下文化的本质。也就是说，人类语言的某种解释是由谁和何处以及其他词围绕评论的方式来解释的。该项目的挑战在于设计手段，以通过破译这种空间语言术语来更好地理解人们对空间的看法。这将导致用于消耗地理空间数据的新颖文本和基于音频的接口，例如以直观的方式或对人或更有效地帮助视力障碍的系统的方式进行指示。该项目的技术目的分为两个推力。第一个推力通过使用户在虚拟环境中进行交互来开发众包地理空间语言表达数据的模拟。空间语言表达式和相互作用是使用定量模型捕获的。然后，第二个推力使用这些用户生成的描述来评估几种建模方法，其中包括（i）研究特定的城市环境以识别上下文因素以及（ii）探索神经方法，以建模将语言的问题建模到这种空间上下文。 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.

项目成果

Trustworthiness of Children Stories Generated by Large Language Models

大语言模型生成的儿童故事的可信度

• 发表时间: 2023
• 期刊: Proceedings of the16th International Natural Language Generation Conference 2023
• 作者: Bhandari, P.;Brennan, H. M.
• 通讯作者: Brennan, H. M.