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        <title>Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue
%A Molnar, Aron
%A Jumelet, Jaap
%A Giulianelli, Mario
%A Sinclair, Arabella
%Y Jiang, Jing
%Y Reitter, David
%Y Deng, Shumin
%S Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F molnar-etal-2023-attribution
%X 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.
%R 10.18653/v1/2023.conll-1.18
%U https://aclanthology.org/2023.conll-1.18
%U https://doi.org/10.18653/v1/2023.conll-1.18
%P 254-273

Markdown (Informal)

[Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue](https://aclanthology.org/2023.conll-1.18) (Molnar et al., CoNLL 2023)

• Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue (Molnar et al., CoNLL 2023)

ACL

• Aron Molnar, Jaap Jumelet, Mario Giulianelli, and Arabella Sinclair. 2023. Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue. In Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), pages 254–273, Singapore. Association for Computational Linguistics.