mlsanityData to 14 Oct 2026

Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction

arXiv 2104.09033 ↗First posted 19 Apr 2021cs.CLcited by 6

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Abstract (arXiv)

This paper asks whether a distinction between production-based and perception-based grammar induction influences either (i) the growth curve of grammars and lexicons or (ii) the similarity between representations learned from independent sub-sets of a corpus. A production-based model is trained on the usage of a single individual, thus simulating the grammatical knowledge of a single speaker. A perception-based model is trained on an aggregation of many individuals, thus simulating grammatical generalizations learned from exposure to many different speakers. To ensure robustness, the experiments are replicated across two registers of written English, with four additional registers reserved as a control. A set of three computational experiments shows that production-based grammars are significantly different from perception-based grammars across all conditions, with a steeper growth curve that can be explained by substantial inter-individual grammatical differences.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 7 of its 35 references that are in the map. First posted on arXiv 19 Apr 2021.

Builds on

7 of 35 references.

References · 7

  1. 2018Learning Word Vectors for 157 Languagescited by 280
  2. 2019Modeling Global Syntactic Variation in English Using Dialect Classificationcited by 13
  3. 2019Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammarcited by 11
  4. 2021Finding Variants for Construction-Based Dialectometry: A Corpus-Based Approach to Regional CxGscited by 11
  5. 2021Global Syntactic Variation in Seven Languages: Towards a Computational Dialectologycited by 11
  6. 2014A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Taggingcited by 9
  7. 2019Modeling the Complexity and Descriptive Adequacy of Construction Grammarscited by 9

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 0; 2025: 1.

6 papers.