Before revision:  It has been found that the presence of a noun between the verb and the main subject , whose grammatical number is opposite to that of the main subject, can cause speakers to produce a verb that agrees with the intervening nounrather than the main noun; the former thus acts as an agreement attractor. Such attractors have also been shown to pose a challenge for RNN models without explicit hierarchical bias to perform well on SVA tasks.Previous work suggests that syntactic cues in the input can aid such models to choose hierarchical rules over linear rules for number agreement. In this work, we investigate the effects of the choice of training data, training algorithm, and architecture on hierarchical generalization. We observe that the models under consideration fail to perform well on sentences with no agreement attractor when trained solely on natural sentences with at least one attractor.
After revision:  It has been found that the presence of a noun between the verb and the main subject with grammatical number opposite to the models under consideration fail to perform well on sentences with no agreement attractor when trained solely on natural sentences with at least one attractor.