Unlike current state-of-the-art language models, young children actively acquire language through interactions with their surrounding environment and caretakers. One mechanism that has been argued to be critical to language learning is the ability to infer the mental states of other agents in social environments, coined Theory of Mind (ToM) by Premack & Woodruff (1978). Drawing inspiration from the modern operationalized versions of ToM implemented in Rabinowitz et al. (2018) and Zhu et al. (2021), we build language-learning agents equipped with ToM, and measure its effects on the learning process. We model ToM by giving the speaker agent an internal listener model that is trained alongside the speaker and used to rerank potential utterances. We experiment with varying task difficulty, hypothesizing that models will acquire more complex language to adapt to stronger environmental pressures. We find that training speakers with a highly weighted ToM listener component leads to performance gains in our image referential game setting. We also find some evidence that increasing task difficulty in the training process results in more fluent and precise utterances in evaluation. This suggests the potential utility of further incorporating ToM, as well as other insights from child language acquisition, into computational models of language acquisition.

翻译：与当前最先进的语言模型不同，幼儿通过与其周围环境和照顾者的互动主动习得语言。一种被认为对语言学习至关重要的机制是推断社会环境中其他智能体心理状态的能力，普雷马克和伍德拉夫（1978）将其称为"心智理论"（Theory of Mind, ToM）。受拉比诺维茨等人（2018）及朱等人（2021）实现的现代可操作化ToM版本的启发，我们构建了配备ToM的语言学习智能体，并衡量其对学习过程的影响。通过赋予说话者智能体一个与说话者共同训练并用于对潜在话语进行重排序的内部听者模型，我们对ToM进行建模。我们实验了不同任务难度，假设模型将习得更复杂的语言以适应更强的环境压力。研究发现，在图像指称游戏场景中，使用高权重ToM听者组件训练说话者智能体可带来性能提升。我们还发现，训练过程中增加任务难度可在评估阶段产生更流畅、更精确的话语。这表明将ToM及儿童语言习得的其他见解进一步融入计算语言习得模型的潜在效用。