Papers with diacritic restoration
Igbo Diacritic Restoration using Embedding Models (N18-4)
Copied to clipboard
| Challenge: | Igbo is a low-resource language spoken by approximately 30 million people worldwide. |
| Approach: | They propose to use word embeddings to restore diacritics in Igbo by using a pre-processing task that replaces missing diacrittics on words from which they have been removed. |
| Outcome: | The embedding models performed better than n-gram models on the diacritic restoration task. |
A System for Diacritizing Four Varieties of Arabic (D19-3)
Copied to clipboard
| 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 . |
Efficient Convolutional Neural Networks for Diacritic Restoration (D19-1)
Copied to clipboard
| 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. |
Automatic Restoration of Diacritics for Speech Data Sets (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing text-based diacritic restoration models have high diacritical error rates when applied to speech data . a recent study shows that the lack of diacritized text can cause poor performance for text restoration models. |
| Approach: | They propose to use Arabic scripts as input for automatic diacritic restoration models . they use a pre-trained model to produce rough diacritized Arabic transcripts for the model . |
| Outcome: | The proposed framework consistently improves diacritic restoration performance compared to baseline models. |
A Multitask Learning Approach for Diacritic Restoration (2020.acl-main)
Copied to clipboard
| 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. |