Papers with Diacritic restoration
Igbo Diacritic Restoration using Embedding Models (N18-4)
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| 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. |
Efficient Convolutional Neural Networks for Diacritic Restoration (D19-1)
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| 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. |
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. |