论文标题
从计算机视觉系统的表现中预测儿童中的单词学习
Predicting Word Learning in Children from the Performance of Computer Vision Systems
论文作者
论文摘要
对于人类儿童以及机器学习系统，学习单词的关键挑战将单词与其描述的视觉现象联系起来。我们通过使用计算机视觉系统的性能来探讨单词学习的这一方面，以使其难以从视觉提示中学习单词。我们表明，与单词频率的预期影响相比，儿童获得不同类别的单词的年龄与视觉分类和字幕系统的性能相关。计算机视觉系统的性能与人类对单词的具体性的判断有关，这反过来又是儿童单词学习的预测指标，这表明这些模型正在捕获单词和视觉现象之间的关系。
For human children as well as machine learning systems, a key challenge in learning a word is linking the word to the visual phenomena it describes. We explore this aspect of word learning by using the performance of computer vision systems as a proxy for the difficulty of learning a word from visual cues. We show that the age at which children acquire different categories of words is correlated with the performance of visual classification and captioning systems, over and above the expected effects of word frequency. The performance of the computer vision systems is correlated with human judgments of the concreteness of words, which are in turn a predictor of children's word learning, suggesting that these models are capturing the relationship between words and visual phenomena.
