  The paper presents four models submitted to Part 2 of the SIGMORPHON 2021
Shared Task 0, which aims at replicating human judgements on the inflection of
nonce lexemes. Our goal is to explore the usefulness of combining pre-compiled
analogical patterns with an encoder-decoder architecture. Two models are
designed using such patterns either in the input or the output of the network.
Two extra models controlled for the role of raw similarity of nonce inflected
forms to existing inflected forms in the same paradigm cell, and the role of
the type frequency of analogical patterns. Our strategy is entirely endogenous
in the sense that the models appealing solely to the data provided by the
SIGMORPHON organisers, without using external resources. Our model 2 ranks
second among all submitted systems, suggesting that the inclusion of analogical
patterns in the network architecture is useful in mimicking speakers'
predictions.
