Summary of Looks Can Be Deceptive: Distinguishing Repetition Disfluency From Reduplication, by Arif Ahmad et al.
Looks can be Deceptive: Distinguishing Repetition Disfluency from Reduplication
by Arif Ahmad, Mothika Gayathri Khyathi, Pushpak Bhattacharyya
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a large-scale study on reduplication and repetition in speech using computational linguistics. It introduces IndicRedRep, a new dataset containing annotated text from Hindi, Telugu, and Marathi languages. The study evaluates transformer-based models for multi-class token classification of reduplication and repetition, achieving macro F1 scores up to 85.62% in Hindi, 83.95% in Telugu, and 84.82% in Marathi. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how we use words in speech. Reduplication is when we repeat a word to add meaning or importance. Repetition is when we accidentally repeat ourselves because we’re not sure what to say next. This study uses computers to analyze huge amounts of text from Indian languages like Hindi, Telugu, and Marathi. It creates a new dataset with special labels for reduplication and repetition. The results show that computer models can accurately identify these patterns in the language. |
Keywords
» Artificial intelligence » Classification » Token » Transformer
