Grounded and Well-rounded：跨模态与跨语言Grounding研究的方法论途径
Grounded and Well-rounded: A Methodological Approach to the Study of Cross-modal and Cross-lingual Grounding
AI总结：
本文提出一个研究Grounding的方法论框架，通过构建不同输入模态的可比模型样本，揭示了跨模态、跨语言与非Grounding模型在行为上的定性差异。
摘要：
Grounding被认为是朝着开发更完整且真正具备语义能力的人工智能系统发展的关键组件。文献分为两派：部分人认为Grounding能带来质的不同的泛化能力，另一些人则认为这可以通过单模态数据量来弥补。目前支持或反对任一观点的实证证据十分有限，我们认为这归因于研究Grounding及其对NLP系统影响时所面临的方法论挑战。本文建立了一个方法论框架，用于研究为模型提供比纯文本更丰富的输入源会带来何种影响（如果有的话）。其核心在于构建在不同输入模态下训练的模型群体的可比样本，以便将不同输入源的定性影响与可量化的模型性能区分开来。使用该框架的实验揭示了跨模态Grounding、跨语言Grounding和非Grounding模型之间在行为上的定性差异，我们根据语义的具体程度，在全球数据集层面以及特定词表示层面测量了这些差异。
英文摘要：
Grounding has been argued to be a crucial component towards the development of more complete and truly semantically competent artificial intelligence systems. Literature has divided into two camps: While some argue that grounding allows for qualitatively different generalizations, others believe it can be compensated by mono-modal data quantity. Limited empirical evidence has emerged for or against either position, which we argue is due to the methodological challenges that come with studying grounding and its effects on NLP systems. In this paper, we establish a methodological framework for studying what the effects are - if any - of providing models with richer input sources than text-only. The crux of it lies in the construction of comparable samples of populations of models trained on different input modalities, so that we can tease apart the qualitative effects of different input sources from quantifiable model performances. Experiments using this framework reveal qualitative differences in model behavior between cross-modally grounded, cross-lingually grounded, and ungrounded models, which we measure both at a global dataset level as well as for specific word representations, depending on how concrete their semantics is.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
