  Analogical proportions are statements of the form "A is to B as C is to D".
They constitute an inference tool that provides a logical framework to address
learning, transfer, and explainability concerns and that finds useful
applications in artificial intelligence and natural language processing. In
this paper, we address two problems, namely, analogy detection and resolution
in morphology. Multiple symbolic approaches tackle the problem of analogies in
morphology and achieve competitive performance. We show that it is possible to
use a data-driven strategy to outperform those models. We propose an approach
using deep learning to detect and solve morphological analogies. It encodes
structural properties of analogical proportions and relies on a specifically
designed embedding model capturing morphological characteristics of words. We
demonstrate our model's competitive performance on analogy detection and
resolution over multiple languages. We provide an empirical study to analyze
the impact of balancing training data and evaluate the robustness of our
approach to input perturbation.
