mlsanityData to 14 Oct 2026

A surprisal--duration trade-off across and within the world's languages

arXiv 2109.15000 ↗First posted 30 Sep 2021cs.CLcited by 23

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Abstract (arXiv)

While there exist scores of natural languages, each with its unique features and idiosyncrasies, they all share a unifying theme: enabling human communication. We may thus reasonably predict that human cognition shapes how these languages evolve and are used. Assuming that the capacity to process information is roughly constant across human populations, we expect a surprisal--duration trade-off to arise both across and within languages. We analyse this trade-off using a corpus of 600 languages and, after controlling for several potential confounds, we find strong supporting evidence in both settings. Specifically, we find that, on average, phones are produced faster in languages where they are less surprising, and vice versa. Further, we confirm that more surprising phones are longer, on average, in 319 languages out of the 600. We thus conclude that there is strong evidence of a surprisal--duration trade-off in operation, both across and within the world's languages.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 8 of its 58 references that are in the map. First posted on arXiv 30 Sep 2021.

Builds on

8 of 58 references.

References · 8

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2020On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behaviorcited by 119
  3. 2018Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Contextcited by 113
  4. 2021Revisiting the Uniform Information Density Hypothesiscited by 68
  5. 2020Phonotactic Complexity and its Trade-offscited by 23
  6. 2018On the Complexity and Typology of Inflectional Morphological Systemscited by 15
  7. 2020A Corpus for Large-Scale Phonetic Typologycited by 9
  8. 2021Disambiguatory Signals are Stronger in Word-initial Positionscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 8; 2024: 2; 2025: 3; 2026: 6.

23 papers.

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  1. 2022Locally Typical Samplingcited by 111
  2. 2023Testing the Predictions of Surprisal Theory in 11 Languagescited by 77
  3. 2023Information Value: Measuring Utterance Predictability as Distance from Plausible Alternativescited by 23
  4. 2023A Cross-Linguistic Pressure for Uniform Information Density in Word Ordercited by 17
  5. 2022On the probability-quality paradox in language generationcited by 16
  6. 2023An Information-Theoretic Analysis of Self-supervised Discrete Representations of Speechcited by 15
  7. 2023Quantifying the redundancy between prosody and textcited by 12
  8. 2026The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?cited by 11
  9. 2022Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinshipcited by 9
  10. 2022Grammatical cues to subjecthood are redundant in a majority of simple clauses across languagescited by 5
  11. 2023Revisiting the Optimality of Word Lengthscited by 5
  12. 2023Information-Theoretic Characterization of Vowel Harmony: A Cross-Linguistic Study on Word Listscited by 4
11 more
  1. 2025The time scale of redundancy between prosody and linguistic contextcited by 4
  2. 2024Computational Sentence-level Metrics Predicting Human Sentence Comprehensioncited by 2
  3. 2024Language models emulate certain cognitive profiles: An investigation of how predictability measures interact with individual differencescited by 2
  4. 2023Predicting Human Translation Difficulty with Neural Machine Translationcited by 1
  5. 2025Uniform Information Density and Syntactic Reduction: Revisiting textitthat-Mentioning in English Complement Clausescited by 1
  6. 2026HOMURA: Taming the Sand-Glass for Time-Constrained LLM Translation via Reinforcement Learningcited by 1
  7. 2026The Distribution of Phoneme Frequencies across the World's Languages: Macroscopic and Microscopic Information-Theoretic Modelscited by 1
  8. 2026Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialoguecited by 1
  9. 2025Surprisal reveals diversity gaps in image captioning and different scorers change the storycited by 0
  10. 2026Modelling the Diachronic Emergence of Phoneme Frequency Distributionscited by 0
  11. 2026Estimating Surprisal from the Speech Signalcited by 0