  Sequential neural networks models are powerful tools in a variety of Natural
Language Processing (NLP) tasks. The sequential nature of these models raises
the questions: to what extent can these models implicitly learn hierarchical
structures typical to human language, and what kind of grammatical phenomena
can they acquire?
  We focus on the task of agreement prediction in Basque, as a case study for a
task that requires implicit understanding of sentence structure and the
acquisition of a complex but consistent morphological system. Analyzing
experimental results from two syntactic prediction tasks -- verb number
prediction and suffix recovery -- we find that sequential models perform worse
on agreement prediction in Basque than one might expect on the basis of a
previous agreement prediction work in English. Tentative findings based on
diagnostic classifiers suggest the network makes use of local heuristics as a
proxy for the hierarchical structure of the sentence. We propose the Basque
agreement prediction task as challenging benchmark for models that attempt to
learn regularities in human language.
