  Language models are often used as the backbone of modern dialogue systems.
These models are pre-trained on large amounts of written fluent language.
Repetition is typically penalised when evaluating language model generations.
However, it is a key component of dialogue. Humans use local and partner
specific repetitions; these are preferred by human users and lead to more
successful communication in dialogue. In this study, we evaluate (a) whether
language models produce human-like levels of repetition in dialogue, and (b)
what are the processing mechanisms related to lexical re-use they use during
comprehension. We believe that such joint analysis of model production and
comprehension behaviour can inform the development of cognitively inspired
dialogue generation systems.
