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Looks can be Deceptive: Distinguishing Repetition Disfluency from Reduplication

arXiv 2407.08147 ↗First posted 11 Jul 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

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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References · 8

  1. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
  2. 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 544
  3. 2016Disfluency Detection using a Bidirectional LSTMcited by 31
  4. 2020Improving Disfluency Detection by Self-Training a Self-Attentive Modelcited by 25
  5. 2019Multi-Task Self-Supervised Learning for Disfluency Detectioncited by 20
  6. 2022HiNER: A Large Hindi Named Entity Recognition Datasetcited by 12
  7. 2018Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Modelcited by 11
  8. 2022LARD: Large-scale Artificial Disfluency Generationcited by 7

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