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.

使用一系列神经视觉语法归纳模型，通过同时学习句法和语义来展示语义引导和句法引导对语言习得的影响，并表明联合学习能够提高语法归纳、词类学习和新句子和动词意义的解释。联合学习通过相互约束句法和语义的假设空间，使得语言习得对学习者更容易。同时，研究联合推理的动态过程对于语言建模和学习研究来说是一个重要的新方向，在认知科学和人工智能领域可以帮助我们解释如何在更受限的学习环境中学习语言。