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.

与空间信息相关的用户体验目前与数据的表示直接相关;也就是说，地理坐标在地图上精确定位，路由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，学习和定性因素的推理，包括空间概念，如近/远，后面，旁边，里面。考虑到空间信息是如何在自然语言中传达的，空间表达能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高度语境化的本质;也就是说，人类语言的部分解释是由谁和在哪里说的，以及评论周围的其他词。这个项目的挑战是设计出一种方法，通过破译这种空间语言术语来更好地理解人们对空间的感知。这将为地理空间数据的消费带来新的基于文本和音频的界面，例如以人们直观的方式询问或指示方向，或为更有效地帮助视障者的系统提供帮助。第一个推力开发了一个模拟，通过让用户在虚拟环境中进行交互，众包地理空间语言表达数据。空间语言的表达和相互作用被捕获使用定量模型。第二个推力，然后使用这些用户生成的描述，以评估几种建模方法，包括（i）研究特定的城市设置，以确定上下文因素和（ii）探索神经方法来建模的问题接地语言，这个空间背景。由此产生的模型，然后可以用来自动翻译语言的地理空间信息，并在相反的方向，训练对话代理，可以产生丰富的，上下文的路线和场景描述与自然，有用的地理空间语言expressions.This奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

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.