Rapid progress in machine learning for natural language processing has the potential to transform debates about how humans learn language. However, the learning environments and biases of current artificial learners and humans diverge in ways that weaken the impact of the evidence obtained from learning simulations. For example, today's most effective neural language models are trained on roughly one thousand times the amount of linguistic data available to a typical child. To increase the relevance of learnability results from computational models, we need to train model learners without significant advantages over humans. If an appropriate model successfully acquires some target linguistic knowledge, it can provide a proof of concept that the target is learnable in a hypothesized human learning scenario. Plausible model learners will enable us to carry out experimental manipulations to make causal inferences about variables in the learning environment, and to rigorously test poverty-of-the-stimulus-style claims arguing for innate linguistic knowledge in humans on the basis of speculations about learnability. Comparable experiments will never be possible with human subjects due to practical and ethical considerations, making model learners an indispensable resource. So far, attempts to deprive current models of unfair advantages obtain sub-human results for key grammatical behaviors such as acceptability judgments. But before we can justifiably conclude that language learning requires more prior domain-specific knowledge than current models possess, we must first explore non-linguistic inputs in the form of multimodal stimuli and multi-agent interaction as ways to make our learners more efficient at learning from limited linguistic input.

翻译：自然语言处理领域的机器学习快速发展，有望改变关于人类如何习得语言的争论。然而，当前人工学习者的学习环境和偏差与人类存在差异，这削弱了从学习模拟中获取证据的影响力。例如，当今最有效的神经语言模型训练所用的语言数据量是典型儿童可获得数据量的约一千倍。为提高计算模型可学习性结果的相关性，我们需要训练不具备显著优于人类之优势的模型学习者。若合适的模型成功习得某些目标语言知识，则可提供概念证明，表明该目标在假设的人类学习场景中具有可学习性。合理的模型学习者将使我们能够进行实验操控，对学习环境中的变量做出因果推断，并严格检验基于可学习性推测而主张人类先天语言知识的刺激贫乏论论点。由于实践和伦理考量，对人类受试者无法进行类似实验，这使得模型学习者成为不可或缺的资源。迄今为止，剥夺当前模型不公平优势的尝试在可接受性判断等关键语法行为上仅获得亚人类结果。但在我们有充分理由断定语言学习需要比当前模型所拥有更多先天领域特定知识之前，必须首先探索以多模态刺激和多智能体交互形式呈现的非语言输入，以使我们的学习者能更高效地从有限语言输入中学习。