arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2109.06129cs.CVcs.CL
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语言模型能编码无基础的感知结构吗?色彩案例研究

Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color

University of Copenhagen(哥本哈根大学);Uppsala University(乌普萨拉大学);University of Trento(特伦托大学);Brown University(布朗大学)

Mostafa Abdou, Artur Kulmizev, Daniel Hershcovich, Stella Frank, Ellie Pavlick, Anders Søgaard

摘要:

预训练的语言模型已经被证明可以对关系信息进行编码,例如知识库中实体或概念之间的关系(Paris,Capital,France)。然而,这种类型的简单关系通常可以被恢复,并且模型在多大程度上隐含地反映了基于世界的拓扑结构,例如感知结构,是未知的。为了探讨这个问题,我们对色彩进行了深入的案例研究。也就是说,我们采用了一个数据集的monolithic颜色项和颜色芯片表示在CIELAB,一个颜色空间的感知有意义的距离度量。使用两种方法来评估在这个空间中的颜色与文本派生的颜色项表示的结构对齐,我们发现显着的对应关系。分析整个色谱中对齐的差异,我们发现,温暖的颜色平均来说比凉爽的颜色更好地与感知颜色空间对齐,这与最近关于颜色命名中有效沟通的研究结果有着有趣的联系。进一步的分析表明,在对齐的差异,在一定程度上,介导的搭配和句法用法的差异,提出问题的颜色感知和使用和上下文之间的关系。

英文摘要:

Pretrained language models have been shown to encode relational information, such as the relations between entities or concepts in knowledge-bases -- (Paris, Capital, France). However, simple relations of this type can often be recovered heuristically and the extent to which models implicitly reflect topological structure that is grounded in world, such as perceptual structure, is unknown. To explore this question, we conduct a thorough case study on color. Namely, we employ a dataset of monolexemic color terms and color chips represented in CIELAB, a color space with a perceptually meaningful distance metric. Using two methods of evaluating the structural alignment of colors in this space with text-derived color term representations, we find significant correspondence. Analyzing the differences in alignment across the color spectrum, we find that warmer colors are, on average, better aligned to the perceptual color space than cooler ones, suggesting an intriguing connection to findings from recent work on efficient communication in color naming. Further analysis suggests that differences in alignment are, in part, mediated by collocationality and differences in syntactic usage, posing questions as to the relationship between color perception and usage and context.

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。

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