  Semantic and syntactic bootstrapping posit that children use their prior
knowledge of one linguistic domain, say syntactic relations, to help later
acquire another, such as the meanings of new words. Empirical results
supporting both theories may tempt us to believe that these are different
learning strategies, where one may precede the other. Here, we argue that they
are instead both contingent on a more general learning strategy for language
acquisition: joint learning. Using a series of neural visually-grounded grammar
induction models, we demonstrate that both syntactic and semantic bootstrapping
effects are strongest when syntax and semantics are learnt simultaneously.
Joint learning results in better grammar induction, realistic lexical category
learning, and better interpretations of novel sentence and verb meanings. Joint
learning makes language acquisition easier for learners by mutually
constraining the hypotheses spaces for both syntax and semantics. Studying the
dynamics of joint inference over many input sources and modalities represents
an important new direction for language modeling and learning research in both
cognitive sciences and AI, as it may help us explain how language can be
acquired in more constrained learning settings.
