EAGER: Shared Gaze in Collaborative Referring

EAGER：协作引用中的共同目光

• 批准号: 1050004
• 负责人: Joyce Chai
• 金额: $ 10万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1050004&HistoricalAwards=false
• 关键词: EAGER; Shared; Gaze; Collaborative; Referring

In situated dialogue, although artificial agents and their human partners are copresent in a shared environment, their knowledge and representation of the shared world are significantly different. When a shared basis of the environment is missing, communication between partners become more challenging. Language alone can be difficult and inefficient for partners to ground objects of interest. Motivated by previous empirical findings on eye gaze in joint attention, in collaboration, and in human language processing, our hypothesis is that eye gaze plays an important role in coordinating the collaborative referring process, especially between partners who have mismatched representations of their shared environment. Based on this hypothesis, the objective of this exploratory project is to examine the role of shared gaze in the collaborative referring process.This EArly Grant for Exploratory Research aims to generate new findings on how shared gaze coordinates the collaborative referring behaviors between partners with mismatched representation of the shared environment. These findings will provide insight to computational approaches and systems that combine gaze modeling with the collaborative discourse to ground references. The collected data will support many in-depth studies on language processing in situated dialogue.

在情境对话中，虽然人工智能体和他们的人类伙伴在一个共享的环境中，他们的知识和共享世界的表示是显着不同的。当缺乏共同的环境基础时，合作伙伴之间的沟通变得更具挑战性。语言本身可能是困难的，效率低下的合作伙伴地面感兴趣的对象。基于以往的经验研究结果的眼睛注视在联合注意，在合作，并在人类的语言处理，我们的假设是，眼睛注视起着重要的作用，在协调合作的指涉过程中，特别是合作伙伴之间谁有不匹配的表示，他们的共享环境。基于这一假设，本探索性项目的目的是研究共享凝视在协作转介过程中的作用，EArly探索性研究基金旨在获得关于共享凝视如何协调具有不匹配共享环境表征的合作伙伴之间的协作转介行为的新发现。这些发现将提供洞察力的计算方法和系统，结合联合收割机凝视建模与协作话语地面参考。收集的数据将支持许多深入的研究语言处理的情景对话。