Dialogue participants may have varying levels of knowledge about the topic
under discussion. In such cases, it is essential for speakers to adapt their
utterances by taking their audience into account. Yet, it is an open question
how such adaptation can be modelled in computational agents. In this paper, we
model a visually grounded referential game between a knowledgeable speaker and
a listener with more limited visual and linguistic experience. Inspired by
psycholinguistic theories, we endow our speaker with the ability to adapt its
referring expressions via a simulation module that monitors the effectiveness
of planned utterances from the listener's perspective. We propose an adaptation
mechanism building on plug-and-play approaches to controlled language
generation, where utterance generation is steered on the fly by the simulator
without finetuning the speaker's underlying language model. Our results and
analyses show that our approach is effective: the speaker's utterances become
closer to the listener's domain of expertise, which leads to higher
communicative success.

本文提出一种基于心理语言学理论的适应性模型，通过模拟模块监控计划发言的有效性并实时控制话语生成，实现知识丰富的说话者对具有更有限语言和视觉经验的听者进行言语适应，以提高沟通成功率。