Looks can be Deceptive: Distinguishing Repetition Disfluency from Reduplication

Arif Ahmad, Mothika Gayathri Khyathi, Pushpak Bhattacharyya
7/11/2024
cs.CLcs.AI

No official code found. Best implementation: arifahmad-py/IndicRedRep (0 stars, MIT, last commit 2024-12-12).

Abstract

Reduplication and repetition, though similar in form, serve distinct linguistic purposes. Reduplication is a deliberate morphological process used to express grammatical, semantic, or pragmatic nuances, while repetition is often unintentional and indicative of disfluency. This paper presents the first large-scale study of reduplication and repetition in speech using computational linguistics. We introduce IndicRedRep, a new publicly available dataset containing Hindi, Telugu, and Marathi text annotated with reduplication and repetition at the word level. We evaluate transformer-based models for multi-class reduplication and repetition token classification, utilizing the Reparandum-Interregnum-Repair structure to distinguish between the two phenomena. Our models achieve macro F1 scores of up to 85.62% in Hindi, 83.95% in Telugu, and 84.82% in Marathi for reduplication-repetition classification.

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MIT

Looks can be Deceptive: Distinguishing Repetition Disfluency from Reduplication (COLING 2025)

00Dec 12, 20241 years agoMIT

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Cite this paper

@article{ahmad2024looks,
  title  = {Looks can be Deceptive: Distinguishing Repetition Disfluency from Reduplication},
  author = {Arif Ahmad and Mothika Gayathri Khyathi and Pushpak Bhattacharyya},
  year   = {2024},
  eprint = {2407.08147},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.08147v1}
}

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