Investigating hybrid approaches for Arabic text diacritization with recurrent neural networks

Alqudah, Saba'; Abandah, Gheith A.; Arabiyat, Alaa

doi:10.1109/aeect.2017.8257765

Cited by 16 publications

(7 citation statements)

References 18 publications

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“…Saba' Alqudah et al [19] followed also the hybrid approach by fusing the MADAMIRA analyzer and a deep bidirectional LSTM network. e outputs of MADAMIRA with a high confidence parameter are input to the network.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Some Automatic Arabic Text Diacritization Systems

Mijlad

Younoussi

2022

Advances in Human-Computer Interaction

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Arabic diacritization is the task of restoring diacritics or vowels for Arabic texts considering that they are mostly written without them. This task, when automated, shows better results for some natural language processing tasks; hence, it is necessary for the field of Arabic language processing. In this paper, we are going to present a comparative study of some automatic diacritization systems. One uses a variant of the hidden Markov model. The other one is a pipeline, which includes a Long Short-Term Memory deep learning model, a rule-based correction component, and a statistical-based component. Additionally, we are proposing some modifications to those systems. We have trained and tested those systems in the same benchmark dataset based on the same evaluation metrics proposed in previous work. The best system results are 9.42% and 22.82% for the diacritic error rate DER and the word error rate WER, respectively.

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Section: Related Workmentioning

confidence: 99%

A Comparative Study of Some Automatic Arabic Text Diacritization Systems

Mijlad

Younoussi

2022

Advances in Human-Computer Interaction

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“…For Arabic diacritization, deep learning approaches usually do not require any prior preprocessing or morphological analysis of the dataset [ 20 ]. In [ 21 ], it was accelerated using hybrid approaches where a morphological and syntactical analyzer is used to assist the neural networks.…”

Section: Related Workmentioning

confidence: 99%

Arabic Syntactic Diacritics Restoration Using BERT Models

Nazih

Hifny

2022

Computational Intelligence and Neuroscience

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The Arabic syntactic diacritics restoration problem is often solved using long short-term memory (LSTM) networks. Handcrafted features are used to augment these LSTM networks or taggers to improve performance. A transformer-based machine learning technique known as bidirectional encoder representations from transformers (BERT) has become the state-of-the-art method for natural language understanding in recent years. In this paper, we present a novel tagger based on BERT models to restore Arabic syntactic diacritics. We formulated the syntactic diacritics restoration as a token sequence classification task similar to named-entity recognition (NER). Using the Arabic TreeBank (ATB) corpus, the developed BERT tagger achieves a 1.36% absolute case-ending error rate (CEER) over other systems.

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“…Error correction techniques were used as a post processing step to the output of the network to overcome some transcription errors. We also experimented preprocessing the RNN input using a morphological and syntactical analyzer in [21]. Mubarak et al [22] implemented a sequence-to-sequence model using an encoder-decoder LSTM RNN with contentbased attention.…”

Section: Example 2 In Tablementioning

confidence: 99%

“…These include speech recognition, machine translation, and text to speech [28]. Arabic text diacritization has been expressed successfully as a sequence transcription problem as well [20][21][22][23][24][25][26][27]. In our work, an input sequence X consists of characters x 1 , x 2 , x 3 , … .…”

Section: Sequence Transcriptionmentioning

confidence: 99%

On the Training of Deep Neural Networks for Automatic Arabic-Text Diacritization

Karim¹,

Abandah²

2021

IJACSA

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Automatic Arabic diacritization is one of the most important and challenging problems in Arabic natural language processing (NLP). Recurrent neural networks (RNNs) have proved recently to achieve state-of-the-art results for sequence transcription problems in general, and Arabic diacritization in specific. In this work, we investigate the effect of varying the size of the training corpus on the accuracy of diacritization. We produce a cleaned corpus of approximately 550k sequences extracted from the full dataset of Tashkeela and use subsets of this corpus in our training experiments. Our base model is a deep bidirectional long short-term memory (BiLSTM) RNN that transcribes undiacritized sequences of Arabic letters with fully diacritized sequences. Our experiments show that error rates improve as the size of training corpus increases. Our best performing model achieves average diacritic and word error rates of 1.45% and 3.89%, respectively. When compared with state-of-the-art diacritization systems, we reduce the word error rate by 12% over the best published results.

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Investigating hybrid approaches for Arabic text diacritization with recurrent neural networks

Cited by 16 publications

References 18 publications

A Comparative Study of Some Automatic Arabic Text Diacritization Systems

A Comparative Study of Some Automatic Arabic Text Diacritization Systems

Arabic Syntactic Diacritics Restoration Using BERT Models

On the Training of Deep Neural Networks for Automatic Arabic-Text Diacritization

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