  Deep learning models struggle with compositional generalization, i.e. the
ability to recognize or generate novel combinations of observed elementary
concepts. In hopes of enabling compositional generalization, various
unsupervised learning algorithms have been proposed with inductive biases that
aim to induce compositional structure in learned representations (e.g.
disentangled representation and emergent language learning). In this work, we
evaluate these unsupervised learning algorithms in terms of how well they
enable compositional generalization. Specifically, our evaluation protocol
focuses on whether or not it is easy to train a simple model on top of the
learned representation that generalizes to new combinations of compositional
factors. We systematically study three unsupervised representation learning
algorithms - $\beta$-VAE, $\beta$-TCVAE, and emergent language (EL)
autoencoders - on two datasets that allow directly testing compositional
generalization. We find that directly using the bottleneck representation with
simple models and few labels may lead to worse generalization than using
representations from layers before or after the learned representation itself.
In addition, we find that the previously proposed metrics for evaluating the
levels of compositionality are not correlated with actual compositional
generalization in our framework. Surprisingly, we find that increasing pressure
to produce a disentangled representation produces representations with worse
generalization, while representations from EL models show strong compositional
generalization. Taken together, our results shed new light on the compositional
generalization behavior of different unsupervised learning algorithms with a
new setting to rigorously test this behavior, and suggest the potential
benefits of delevoping EL learning algorithms for more generalizable
representations.
