Summary of Estimating the Level Of Dialectness Predicts Interannotator Agreement in Multi-dialect Arabic Datasets, by Amr Keleg et al.
Estimating the Level of Dialectness Predicts Interannotator Agreement in Multi-dialect Arabic Datasets
by Amr Keleg, Walid Magdy, Sharon Goldwater
First submitted to arxiv on: 18 May 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 A novel approach to annotating multi-dialect Arabic datasets is proposed in this paper, which aims to improve the quality of these datasets by routing dialectal samples to native speakers of their respective dialects. The study focuses on the relationship between the Arabic Level of Dialectness (ALDi) scores and the agreement among annotators for sentence-classification tasks. The authors hypothesize that samples with higher ALDi scores are harder to label when assigned to annotators who do not speak the dialect, which is supported by strong evidence across 11 out of 15 public datasets analyzed. To achieve this, automatic identification of dialects at higher accuracies is recommended. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Arabic speakers have different accents and ways of speaking, making it hard to understand each other’s language. This paper looks at how to make Arabic text datasets better by matching the right people with the right words. They found that sentences that are very different from standard Arabic (called high ALDi scores) are harder for non-native speakers to agree on what they mean. By studying 15 different public datasets, they showed that this is true for many of them. |
Keywords
» Artificial intelligence » Classification
