Derivationally related words, such as "runner" and "running", exhibit
semantic differences which also elicit different visual scenarios. In this
paper, we ask whether Vision and Language (V\&L) models capture such
distinctions at the morphological level, using a a new methodology and dataset.
We compare the results from V\&L models to human judgements and find that
models' predictions differ from those of human participants, in particular
displaying a grammatical bias. We further investigate whether the human-model
misalignment is related to model architecture. Our methodology, developed on
one specific morphological contrast, can be further extended for testing models
on capturing other nuanced language features.

通过对相关词汇、视觉和语言模型、形态学层面、人机模型不匹配以及微妙的语言特征的研究，该论文揭示了视觉和语言模型在捕捉形态学差异时存在的差异和模型的语法倾向。该研究方法不仅可以拓展到其他细微语言特征的测试，还可以进一步提取准确的关键词。