  Language acquisition is the process of learning words from the surrounding
scene. We introduce a meta-learning framework that learns how to learn word
representations from unconstrained scenes. We leverage the natural
compositional structure of language to create training episodes that cause a
meta-learner to learn strong policies for language acquisition. Experiments on
two datasets show that our approach is able to more rapidly acquire novel words
as well as more robustly generalize to unseen compositions, significantly
outperforming established baselines. A key advantage of our approach is that it
is data efficient, allowing representations to be learned from scratch without
language pre-training. Visualizations and analysis suggest visual information
helps our approach learn a rich cross-modal representation from minimal
examples. Project webpage is available at https://expert.cs.columbia.edu/
