The NLP community has seen substantial recent interest in grounding to facilitate interaction between language technologies and the world. However, as a community, we use the term broadly to reference any linking of text to data or non-textual modality. In contrast, Cognitive Science more formally defines "grounding" as the process of establishing what mutual information is required for successful communication between two interlocutors -- a definition which might implicitly capture the NLP usage but differs in intent and scope. We investigate the gap between these definitions and seek answers to the following questions: (1) What aspects of grounding are missing from NLP tasks? Here we present the dimensions of coordination, purviews and constraints. (2) How is the term "grounding" used in the current research? We study the trends in datasets, domains, and tasks introduced in recent NLP conferences. And finally, (3) How to advance our current definition to bridge the gap with Cognitive Science? We present ways to both create new tasks or repurpose existing ones to make advancements towards achieving a more complete sense of grounding.
Modelling Analogies and Analogical Reasoning: Connecting Cognitive Science Theory and NLP Research
Computation, Cognition & Language Group, Idiap Research Institute（计算、认知与语言组，Idiap研究所）；NLP Lab, EPFL（NLP实验室，EPFL）；Psychological Methods, University of Amsterdam（心理方法，阿姆斯特丹大学）；Faculty of Communication, Culture and Society and Faculty of Informatics Università della Svizzera italiana（传播、文化与社会学院和信息学院，瑞士大学）
TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Wuhan University（武汉大学）；University of North Texas（北卡罗来纳州立大学）；Fudan University（复旦大学）
Modeling Subjectivity in Cognitive Appraisal with Language Models
Queen Mary University of London（伦敦大学玛丽女王学院）；King’s College London（伦敦大学国王学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；The Alan Turing Institute（艾伦·图灵研究所）
Proceedings of the 2018 Workshop on Compositional Approaches in Physics, NLP, and Social Sciences
能够组成部分，形成一个更复杂的整体，并分析一个整体作为一个元素的组合，是跨学科的理想。这个研讨会汇集了研究人员将组合方法应用于物理，NLP，认知科学和博弈论。在NLP中，一个长期的目标是表示单词如何组合成短语和句子。在分布语义学的框架内，词被表示为向量空间中的向量。Coecke et al. [2010]的分类模型受到量子协议的启发，为NLP的向量空间模型中的组合性提供了令人信服的解释。此外，在认知科学中，向量空间模型也有一段历史。Nosofsky [1986]和Smith等人[1988]开发的分类理论利用了特征向量之间距离的概念。最近Gärdenfors [2004，2014]开发了一个概念模型，其中概念空间提供几何结构，信息由向量空间中的点，向量和区域表示。同样的组合方法也被应用于这种形式主义，使概念空间理论比以前更丰富的组合模型[Bolt et al.，2018年]。合成方法也被应用于策略博弈和纳什均衡的研究。与经典博弈论不同，在经典博弈论中，博弈是作为一个全局对象整体研究的，组合博弈论通过从较小的组件构建大型复杂的博弈来自下而上工作。这种方法本身就很困难，因为必须考虑游戏之间的相互作用。最近开始了对该领域的分类合成方法的研究[Ghani等人，2018年]。此外，认知科学、语言学和博弈论这三个学科之间的相互作用是研究的沃土。认知科学中的博弈论是一个成熟的领域[Camerer，…
Perceptual Structure in the Absence of Grounding for LLMs: The Impact of Abstractedness and Subjectivity in Color Language
Rakuten Institute of Technology（乐天技术研究所）；Rakuten Group, Inc（乐天集团）；National Institute of Advanced Industrial Science and Technology（日本产业技术综合研究所）；Graduate School of Engineering, The University of Tokyo（东京大学大学院工学系研究科）
Linguistic-Based Mild Cognitive Impairment Detection Using Informative Loss
Ritchie School of Engineering and Computer Science（工程与计算机科学里奇学院）；DreamFace Technologies LLC（DreamFace科技有限责任公司）；Massachusetts General Hospital（麻省总医院）；Prince Sattam Bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹王子大学）
