  Analogical proportions are statements of the form "A is to B as C is to D"
that are used for several reasoning and classification tasks in artificial
intelligence and natural language processing (NLP). For instance, there are
analogy based approaches to semantics as well as to morphology. In fact,
symbolic approaches were developed to solve or to detect analogies between
character strings, e.g., the axiomatic approach as well as that based on
Kolmogorov complexity. In this paper, we propose a deep learning approach to
detect morphological analogies, for instance, with reinflexion or conjugation.
We present empirical results that show that our framework is competitive with
the above-mentioned state of the art symbolic approaches. We also explore
empirically its transferability capacity across languages, which highlights
interesting similarities between them.
