Papers with Arabic dialect identification
Automated essay scoring with string kernels and word embeddings (P18-2)
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| Challenge: | Existing approaches to automatic essay scoring use low-level character n-gram features. |
| Approach: | They propose to combine string kernels and word embeddings for automatic essay scoring. |
| Outcome: | The proposed method outperforms state-of-the-art deep learning methods in Arabic dialect identification and native language identification tasks. |
Fine-Grained Arabic Dialect Identification (C18-1)
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| Challenge: | Existing work on Arabic Dialect Identification typically targeted coarse-grained five dialect classes plus Standard Arabic at most (6-way classification). |
| Approach: | They propose to tackle a fine-grained Arabic dialect classification task covering 25 cities from across the Arab World, in addition to Standard Arabic. |
| Outcome: | The proposed task can identify the exact city of a speaker at an accuracy of 67.9% for sentences with an average length of 7 words and reach more than 90% when we consider 16 words. |
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set (D18-1)
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| Challenge: | Recent studies have demonstrated remarkable performance in text classification tasks such as Arabic dialect identification. |
| Approach: | They propose two approaches to improve string kernels' accuracy in Arabic and English . first approach interprets pairwise string kernel similarities between training and test sets as features . second approach adapts to training set and adds test samples for another round of training . |
| Outcome: | The proposed methods improve English polarity classification and Arabic dialect identification. |
Arabic Dialect Identification in the Context of Bivalency and Code-Switching (L18-1)
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| Challenge: | Existing methods for identifying Arabic dialects require significant amounts of annotated training data which is costly and time consuming to produce. |
| Approach: | They propose a novel approach to Arabic dialect identification using language bivalency and written code-switching to identify Arabic dialects. |
| Outcome: | The proposed method can reach more than 76% and score well (66%) when tested on unseen data. |