  Reflexive anaphora present a challenge for semantic interpretation: their
meaning varies depending on context in a way that appears to require abstract
variables. Past work has raised doubts about the ability of recurrent networks
to meet this challenge. In this paper, we explore this question in the context
of a fragment of English that incorporates the relevant sort of contextual
variability. We consider sequence-to-sequence architectures with recurrent
units and show that such networks are capable of learning semantic
interpretations for reflexive anaphora which generalize to novel antecedents.
We explore the effect of attention mechanisms and different recurrent unit
types on the type of training data that is needed for success as measured in
two ways: how much lexical support is needed to induce an abstract reflexive
meaning (i.e., how many distinct reflexive antecedents must occur during
training) and what contexts must a noun phrase occur in to support
generalization of reflexive interpretation to this noun phrase?
