Attribution and Alignment: Effects of Local Context Repetition on
  Utterance Production and Comprehension in Dialogue
Aron Molnar Jaap Jumelet Mario Giulianelli Arabella Sinclair
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.