mlsanityData to 14 Oct 2026

Construction Repetition Reduces Information Rate in Dialogue

arXiv 2210.08321 ↗First posted 15 Oct 2022cs.CLcited by 5

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Abstract (arXiv)

Speakers repeat constructions frequently in dialogue. Due to their peculiar information-theoretic properties, repetitions can be thought of as a strategy for cost-effective communication. In this study, we focus on the repetition of lexicalised constructions -- i.e., recurring multi-word units -- in English open-domain spoken dialogues. We hypothesise that speakers use construction repetition to mitigate information rate, leading to an overall decrease in utterance information content over the course of a dialogue. We conduct a quantitative analysis, measuring the information content of constructions and that of their containing utterances, estimating information content with an adaptive neural language model. We observe that construction usage lowers the information content of utterances. This facilitating effect (i) increases throughout dialogues, (ii) is boosted by repetition, (iii) grows as a function of repetition frequency and density, and (iv) is stronger for repetitions of referential constructions.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 9 of its 84 references that are in the map. First posted on arXiv 15 Oct 2022.

Builds on

9 of 84 references.

References · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  3. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  4. 2019Neural Text Generation with Unlikelihood Trainingcited by 503
  5. 2016Deep Reinforcement Learning for Dialogue Generationcited by 288
  6. 2017Dynamic Evaluation of Neural Sequence Modelscited by 55
  7. 2020Human Sentence Processing: Recurrence or Attention?cited by 51
  8. 2018A Neural Model of Adaptation in Readingcited by 18
  9. 2021Taming Repetition in Dialogue Generationcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 2.

5 papers.