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.

本研究为了衡量 Theory of Mind（意念）在语言学习中的影响，在现有 ToM 的版本上，建立了能够融合 ToM 的语言学习代理，并通过实验，证实高度融合 ToM listener 组件的训练，可以在图像指称游戏环境中获得更好的性能，这表明在计算语言习得中进一步结合 ToM 以及儿童语言习得研究的潜在效用。