Modern neural language models (LMs) are powerful tools for modeling human
sentence production and comprehension, and their internal representations are
remarkably well-aligned with representations of language in the human brain.
But to achieve these results, LMs must be trained in distinctly un-human-like
ways -- requiring orders of magnitude more language data than children receive
during development, and without any of the accompanying grounding in
perception, action, or social behavior. Do models trained more naturalistically
-- with grounded supervision -- exhibit more human-like language learning? We
investigate this question in the context of word learning, a key sub-task in
language acquisition. We train a diverse set of LM architectures, with and
without auxiliary supervision from image captioning tasks, on datasets of
varying scales. We then evaluate these models on a broad set of benchmarks
characterizing models' learning of syntactic categories, lexical relations,
semantic features, semantic similarity, and alignment with human neural
representations. We find that visual supervision can indeed improve the
efficiency of word learning. However, these improvements are limited: they are
present almost exclusively in the low-data regime, and sometimes canceled out
by the inclusion of rich distributional signals from text. The information
conveyed by text and images is not redundant -- we find that models mainly
driven by visual information yield qualitatively different from those mainly
driven by word co-occurrences. However, our results suggest that current
multi-modal modeling approaches fail to effectively leverage visual information
to build more human-like word representations from human-sized datasets.

通过对具有意义的监督的视觉数据进行训练，我们发现在具有限定语言数据的情况下，视觉监督可以提高词汇学习的效率，但这种改进是有限的，并且当前的多模态建模方法未能有效利用视觉信息以构建更具人类特征的词汇表示。