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Sequential neural networks models are powerful tools in a variety of NaturalLanguage Processing (NLP) tasks. The sequential nature of these models raisesthe questions: to what extent can these models implicitly learn hierarchicalstructures typical to human language, and what kind of grammatical phenomenacan they acquire?  We focus on the task of agreement prediction in Basque, as a case study for atask that requires implicit understanding of sentence structure and theacquisition of a complex but consistent morphological system. Analyzingexperimental results from two syntactic prediction tasks -- verb numberprediction and suffix recovery -- we find that sequential models perform worseon agreement prediction in Basque than one might expect on the basis of aprevious agreement prediction work in English. Tentative findings based ondiagnostic classifiers suggest the network makes use of local heuristics as aproxy for the hierarchical structure of the sentence. We propose the Basqueagreement prediction task as challenging benchmark for models that attempt tolearn regularities in human language.

Can LSTM Learn to Capture Agreement? The Case of Basque