Human language acquisition is an efficient, supervised, and continual
process. In this work, we took inspiration from how human babies acquire their
first language, and developed a computational process for word acquisition
through comparative learning. Motivated by cognitive findings, we generated a
small dataset that enables the computation models to compare the similarities
and differences of various attributes, learn to filter out and extract the
common information for each shared linguistic label. We frame the acquisition
of words as not only the information filtration process, but also as
representation-symbol mapping. This procedure does not involve a fixed
vocabulary size, nor a discriminative objective, and allows the models to
continually learn more concepts efficiently. Our results in controlled
experiments have shown the potential of this approach for efficient continual
learning of grounded words.

通过比较学习的计算过程，本研究模拟了人类婴儿习得第一门语言的方式，开发了一种用于词汇习得的计算过程，通过筛选和提取共享语言标签的共同信息，并将词汇的习得过程视为信息过滤和符号映射，以实现有效的持续学习。在对照实验中得到的结果显示了这种方法在词汇习得方面的潜力。