The MADAR Shared Task on Arabic Fine-Grained Dialect Identification

Bouamor, Houda; Hassan, Sabit; Habash, Nizar

doi:10.18653/v1/w19-4622

Cited by 72 publications

(87 citation statements)

References 24 publications

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“…In Arabic, it is prerequisite for most NLP tasks, where many subsequent tasks depend on it. We can find spoken dialect identification work in Biadsy et al MADAR shared task (Bouamor et al, 2019) consists of two sub-tasks which are…”

Section: Related Workmentioning

confidence: 99%

“…These native languages or dialects can be categorized based on their common linguistic features and geographical locations . This categorization is described in detail in Bouamor et al (2019). In the technological expansion of communication era, automatic identification of these dialects becomes an essential task for major natural language applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Arabic Dialect Identification for Travel and Twitter Text

Mishra¹,

Mujadia

2019

Proceedings of the Fourth Arabic Natural Language Processing Workshop

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This paper presents the results of the experiments done as a part of MADAR Shared Task in WANLP 2019 on Arabic Fine-Grained Dialect Identification. Dialect Identification is one of the prominent tasks in the field of Natural language processing where the subsequent language modules can be improved based on it. We explored the use of different features like char, word n-gram, language model probabilities, etc on different classifiers. Results show that these features help to improve dialect classification accuracy. Results also show that traditional machine learning classifier tends to perform better when compared to neural network models on this task in a low resource setting.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Arabic Dialect Identification for Travel and Twitter Text

Mishra¹,

Mujadia

2019

Proceedings of the Fourth Arabic Natural Language Processing Workshop

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“…The corpus covers the dialects of 25 Arab cities and the MSA. It is the same data set described in Bouamor et al (2019) and . This corpus is composed of 2000 sentences translated to each dialect, with a total of 52000 sentences.…”

Section: Datamentioning

confidence: 99%

Hierarchical Deep Learning for Arabic Dialect Identification

Francony¹,

Guichard²,

Joshi³

et al. 2019

Proceedings of the Fourth Arabic Natural Language Processing Workshop

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In this paper, we present two approaches for Arabic Fine-Grained Dialect Identification. The first approach is based on Recurrent Neural Networks (BLSTM, BGRU) using hierarchical classification. The main idea is to separate the classification process for a sentence from a given text in two stages. We start with a higher level of classification (8 classes) and then the finer-grained classification (26 classes). The second approach is given by a voting system based on Naive Bayes and Random Forest. Our system achieves an F 1 score of 63.02% on the subtask evaluation dataset.

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“…In this work, we used the MADAR Travel Domain dataset built by translating the Basic Traveling Expression Corpus (BTEC) (Takezawa et al, 2007). The whole sentences have been translated manually from English and French to the different Arabic dialects by speakers of 25 dialects Bouamor et al, 2019). The training data is composed of 1600 sentences for each of the 25 dialects in addition to MSA.…”

Section: Datasetmentioning

confidence: 99%

ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification

Abbas¹,

Lichouri

Freihat

2019

Proceedings of the Fourth Arabic Natural Language Processing Workshop

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This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task. The proposed solution utilized a set of classifiers that we trained on character and word features. These classifiers are: Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Passive Aggressive(PA) and Perceptron (PC). The system achieved competitive results, with a performance of 62.87% and 62.12% for both development and test sets.

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The MADAR Shared Task on Arabic Fine-Grained Dialect Identification

Cited by 72 publications

References 24 publications

Arabic Dialect Identification for Travel and Twitter Text

Arabic Dialect Identification for Travel and Twitter Text

Hierarchical Deep Learning for Arabic Dialect Identification

ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification

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