She adapts to her student: An expert pragmatic speaker tailoring her referring expressions to the Layman listener - Greco, Claudio; Bagade, Diksha; Le, Dieu-Thu; Bernardi, Raffaella (Frontiers in Artificial Intelligence, 2023)
Communication is a dynamic process through which interlocutors adapt to each other. In the development of conversational agents, this core aspect has been put aside for several years since the main challenge was to obtain conversational neural models able to produce utterances and dialogues that at least at surface level are human-like. Now that this milestone has been achieved, the importance of paying attention to the dynamic and adaptive interactive aspect of language has been advocated in several position papers. In this paper, we focus on how a Speaker adapts to an interlocutor with different background knowledge. Our models undergo a pre-training phase, through which they acquire grounded knowledge by learning to describe an image, and an adaptive phase through which a Speaker and a Listener play a repeated reference game. \citet{hawkins2020:continual} study how conversational models create new conventions; we are interested, instead, in studying whether the Speaker learns from the Listener's mistakes to adapt to his background knowledge. We evaluate models based on Rational Speech Act (RSA), a likelihood loss, and a combination of the two. We show that RSA indeed could work as a backbone to drive the Speaker towards the Listener: in the combined model, the communication becomes more successful, in terms of Listener's accuracy, and the language generated by the Speaker features changes that signal an adaptation to the Listener's background knowledge: the captions describing unknown object categories contain more adjectives and less direct reference to the unknown objects.