Transformers for Bridging Persian Dialects: Transliteration Model for Tajiki and Iranian Scripts (2024.lrec-main)
Copied to clipboard
| Challenge: | Despite its profound linguistic and cultural significance, Tajiki Persian remains a low-resource language with scant digitized datasets for computational applications. |
| Approach: | They propose to use Shahnameh, a seminal Persian epic poem, to train and assess Tajiki Persian transliteration models using two prominent sequence-to-sequence architectures: GRU with attention and transformer. |
| Outcome: | The proposed model outperforms pre-trained models with attention and transformer. |
Similar Papers
ParsTranslit: Truly Versatile Tajik-Farsi Transliteration (2026.findings-eacl)
Copied to clipboard
| Challenge: | Despite significant similarities between the two written standards, script differences hinder simple one-to-one mapping, hindering written communication and interaction between Tajikistan and its Persian-speaking “siblings”. |
| Approach: | They propose to use a sequence-to-sequence model to convert between two scripts in a Persian-speaking country using two datasets. |
| Outcome: | The proposed model achieves chrF++ and Normalized CER scores of 87.91 and 0.05 from Farsi to Tajik and 92.28 and 0.04 from Tajikistan to Farsis. |
Matina: A Large-Scale 73B Token Persian Text Corpus (2025.naacl-long)
Copied to clipboard
Sara Bourbour Hosseinbeigi, Fatemeh Taherinezhad, Heshaam Faili, Hamed Baghbani, Fatemeh Nadi, Mostafa Amiri
| Challenge: | Existing Persian datasets are small and lack content diversity . lack of high-quality data has slowed development of NLP models and open-source LLMs for Persian. |
| Approach: | They propose a Persian dataset of 72.9B tokens that is preprocessed and deduplicated to ensure high data quality. |
| Outcome: | The proposed model performs well on key Persian NLP tasks. |
AraT5: Text-to-Text Transformers for Arabic Language Generation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing models that convert text-based language problems into text-to-text format are not suitable for multilingual tasks. |
| Approach: | They propose a unified Transformer framework that converts all language problems into a text-to-text format. |
| Outcome: | The proposed model performs better on all ARGEN tasks than existing models with 49 less data. |
Code-Switching and Back-Transliteration Using a Bilingual Model (2024.findings-eacl)
Copied to clipboard
| Challenge: | a wealth of Judeo-Arabic texts is available on the internet, but most speakers of Arabic are unfamiliar with the Hebrew script. |
| Approach: | They propose two machine-learning models for transliterating Judeo-Arabic into Arabic . they also propose a model for identifying non-Arabian words, predominantly Hebrew and Aramaic . |
| Outcome: | The proposed models outperform previous solutions for transliterating Judeo-Arabic into Arabic and identifying non-Arabian words, mainly Hebrew and Aramaic. |
HarfoSokhan: A Comprehensive Parallel Dataset for Transitions between Persian Colloquial and Formal Variations (2026.eacl-long)
Copied to clipboard
Hamid Jahad Sarvestani, Vida Ramezanian, Saee Saadat, Neda Taghizadeh Serajeh, Maryam Sadat Razavi Taheri, Shohreh Kasaei, MohammadAmin Fazli, Ehsaneddin Asgari
| Challenge: | A wide array of NLP/NLU models have been developed for the Persian language but performance drops when applied to the colloquial form of Persian. |
| Approach: | They propose to use a large-scale colloquial to formal Persian parallel dataset to train a GPT2 model that exhibited remarkable proficiency in colloqual to informal text style transfer. |
| Outcome: | The proposed dataset outperforms OpenAI’s GPT-3.5-turbo model and a leading rule-based system in colloquial to formal Persian conversion. |
GE2PE: Persian End-to-End Grapheme-to-Phoneme Conversion (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing text-to-speech systems struggle to produce natural speech from grapheme sequences . Grapheme-to phoneme conversion (G2P) systems face limitations when dealing with Persian texts due to the complexity of Persian transcription. |
| Approach: | They propose to use phonetic information to enhance the input sequence for Persian translations. |
| Outcome: | The proposed model surpasses state-of-the-art models by 1.86% in word error rate and 3.42% in homograph disambiguation accuracy. |
Challenging the Transformer-based models with a Classical Arabic dataset: Quran and Hadith (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing benchmark datasets have a low readability index which does not reflect real-world complex data. |
| Approach: | They constructed a dataset of Quran-verse and Hadith-teaching pairs by consulting sources of reputable religious experts. |
| Outcome: | The proposed models performed on a binary classification task to identify whether two pieces of CA text convey the same underlying message. |
ManaTTS Persian: a recipe for creating TTS datasets for lower resource languages (2025.naacl-long)
Copied to clipboard
| Challenge: | a new text-to-speech system is needed for visual impairments and the visually impaired . a text-based system is not available for all users, and is therefore limited to a limited audience. |
| Approach: | They propose to use ManaTTS, the most extensive publicly accessible Persian corpus . they use a fully transparent, MIT-licensed pipeline to collect transcribed speech datasets . |
| Outcome: | The proposed framework is the most extensive publicly accessible single-speaker Persian corpus . it includes tools for sentence tokenization, bounded audio segmentation, and forced alignment method . |
MirasText: An Automatically Generated Text Corpus for Persian (L18-1)
Copied to clipboard
Behnam Sabeti, Hossein Abedi Firouzjaee, Ali Janalizadeh Choobbasti, S.H.E. Mortazavi Najafabadi, Amir Vaheb
| Challenge: | Natural language processing is one of the most important fields of artificial intelligence. |
| Approach: | They propose to use MirasText to generate Persian text corpus from Persian websites . MiraSText has over 2.8 million documents and over 1.4 billion tokens . |
| Outcome: | The generated corpus has over 2.8 million documents and over 1.4 billion tokens . MirasText has over 800 billion token tokens and more than 300 thousand articles . |
Parsivar: A Language Processing Toolkit for Persian (L18-1)
Copied to clipboard
| Challenge: | a preprocessing step is required to convert text into a standard format for NLP tasks. |
| Approach: | They propose a Persian preprocessing toolkit that performs various kinds of activities . they use a plagiarism detection system to exploit the proposed toolkit . |
| Outcome: | The proposed tool outperforms available Persian preprocessing tools by about 8 percent in terms of F1 . the proposed toolkit performs normalization, space correction, tokenization, stemming, parts of speech tagging and shallow parsing tasks. |