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:自然语言处理机器学习的快速进展有可能改变关于人类如何学习语言的争论。然而,当前人工学习者和人类的学习环境与偏向差异,从而削弱从学习模拟中获得的证据的影响。例如,今天最有效的神经语言模型的训练大约是典型儿童可获得的语言数据量的一千倍。为了提高计算模型学习结果的相关性,我们需要培训模型学习者,但对人类而言没有显著优势。如果一个适当的模型成功获得一些目标语言知识,它可以证明一个概念,即:在虚伪的人类学习情景中,目标是可以学习的。可见模型学习者将使我们能够进行实验性操纵,对学习环境中的变量作出因果关系推论,并严格测试典型儿童可获得的语言数据数量。为了提高计算模型的可学习性,我们需要培训模型的内在语言知识内含性知识,但基于实际和道德考虑,要让模型学习者成为不可或缺的资源。如此之远,试图剥夺当前模型对学习环境变量的因果性推算,在先行法上,我们必须先行得分流的模型,才能为我们学习。