| Challenge: | Diacritic restoration is a computational task that requires a computer to understand written texts. |
| Approach: | They propose to use Temporal Convolutional Neural Networks (TCN) to restore missing diacritics for each character in written text. |
| Outcome: | The proposed model improves on TCN in Arabic, Yoruba, and Vietnamese. |
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Diacritics Restoration Using Neural Networks (L18-1)
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| Challenge: | a novel combination of character-level recurrent neural network and language model is proposed . people often replace characters with diacritics with their ASCII counterparts . |
| Approach: | They propose a character-level recurrent neural network-based model and a language model for diacritics restoration. |
| Outcome: | The proposed model reduces error of current best systems by 20% to 64% on four languages . it is also able to restore diacritical marks on a number of languages using the same model . |
A Multitask Learning Approach for Diacritic Restoration (2020.acl-main)
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| Challenge: | Diacritics are used to specify pronunciations and meanings in many languages like Arabic. |
| Approach: | They propose to use multi-task learning to optimize diacritic restoration with related NLP problems . they use Arabic as a case study since it has sufficient data resources for tasks . |
| Outcome: | The proposed model outperforms baseline models and is comparable to the state-of-the-art models. |
Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation (D19-52)
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| Challenge: | a number of Arabic text diacritizers use diacritics to convey information about meaning of a word . Arabic text to speech (TTS) requires a complex process to determine the correct diacritical for each character . |
| Approach: | They propose to use Arabic diacritization to enhance machine translation models . they propose to build automatic Arabic text diacritics using two approaches . |
| Outcome: | The proposed models are either better or on par with other models, which require language-dependent post-processing steps, unlike ours. |
Advancing Arabic Diacritization: Improved Datasets, Benchmarking, and State-of-the-Art Models (2025.emnlp-main)
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| Challenge: | Arabic diacritics are typically omitted in written Arabic, leading to ambiguity . authors propose a methodology to analyze and refine a large diacritized corpus . |
| Approach: | They propose a methodology to analyze and refine a large diacritized corpus to improve training quality. |
| Outcome: | The proposed model achieves state-of-the-art results with 3.12% and 2.70% WER on WikiNews-2014 and Wikinews-2024. |
Highly Effective Arabic Diacritization using Sequence to Sequence Modeling (N19-1)
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| Challenge: | Arabic text is written without short vowels (or diacritics) their presence is essential for properly verbalizing Arabic . |
| Approach: | They propose a character-level sequence-to-sequence deep learning model that recovers both types of diacritics without the use of explicit feature engineering. |
| Outcome: | The proposed model outperforms all previous state-of-the-art models on overlapping windows of words . it achieves a word error rate (WER) of 4.49% compared to the state- of-the art systems . |
Dilated Convolutional Neural Networks for Lightweight Diacritics Restoration (2022.lrec-1)
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| Challenge: | Diacritics restoration is a ubiquitous task in the Latin-alphabet-based English-dominated Internet language environment. |
| Approach: | They propose a 1D dilated convolution-based approach which operates on a character-level. |
| Outcome: | The proposed approach surpasses similar models and is competitive with larger models. |
Arabic Diacritization Using Morphologically Informed Character-Level Model (2024.lrec-main)
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Muhammad Morsy Elmallah, Mahmoud Reda, Kareem Darwish, Abdelrahman El-Sheikh, Ashraf Hatim Elneima, Murtadha Aljubran, Nouf Alsaeed, Reem Mohammed, Mohamed Al-Badrashiny
| Challenge: | Diacritics are typically omitted in Arabic writings and the reader needs to guess the proper diacritics as they are reading. |
| Approach: | They propose a morphologically informed character-level model that can recover both types of diacritics simultaneously. |
| Outcome: | The proposed model achieves lowest word-level diacritization error rate for Classical Arabic, MSA, and two dialectal Arabic texts. |
Convolutional Neural Networks with Recurrent Neural Filters (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) use recurrent neural networks as convolution filters to capture language compositionality and long-term dependencies. |
| Approach: | They propose to use recurrent neural networks (RNNs) as convolution filters to capture language compositionality and long-term dependencies. |
| Outcome: | The proposed convolutional neural networks achieve state-of-the-art on two sentences and the Stanford Sentiment Treebank. |
Efficient Sequence Learning with Group Recurrent Networks (N18-1)
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| Challenge: | Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation and speech recognition. |
| Approach: | They propose an efficient architecture to improve the efficiency of such RNN model training by adopting the group strategy for recurrent layers while exploiting the representation rearrangement strategy between layers as well as time steps. |
| Outcome: | The proposed architecture achieves comparable or better accuracy compared with baselines, with a much smaller number of parameters and at a lower computational cost. |
A System for Diacritizing Four Varieties of Arabic (D19-3)
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| Challenge: | Short vowels, aka diacritics, are omitted when writing different varieties of Arabic . diacritization is essential for language learning and text-to-speech applications . |
| Approach: | They propose a system for recovering diacritics in Arabic without short vowels . they use a character-based sequence-to-sequence deep learning model . |
| Outcome: | The proposed system beats all previous SOTA systems for Arabic varieties . it uses a character-based sequence-to-sequence deep learning model . |