Decades of studies trying to define the extent to which artificial neural networks can exhibit
systematicity suggest that systematicity can be achieved by connectionist models but not by default.
Here we present a novel connectionist model of sentence production that employs rich situation
model representations originally proposed for modeling systematicity in comprehension. The high
performance of our model demonstrates that such representations are also well suited to model
language production. Furthermore, the model can produce multiple novel sentences for previously
unseen situations, including in a different voice (actives vs. passive) and with words in new syntactic
roles, thus demonstrating semantic and syntactic generalization and arguably systematicity. Our
results provide yet further evidence that such connectionist approaches can achieve systematicity, in
production as well as comprehension. We propose our positive results to be a consequence of the
regularities of the microworld from which the semantic representations are derived, which provides
a sufficient structure from which the neural network can interpret novel inputs