Elamur Research⌕
☰
home / papers / 2023 / Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation

Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation

Summary

The paper addresses grammatical error correction (GEC) and grammatical error detection (GED) in Arabic (MSA). Data for this morphologically rich language are scarce, and its orthography and punctuation are often inconsistent. The authors are the first to evaluate new pretrained Seq2Seq Transformer models, among them AraBART, on Arabic GEC. They also formulate a multi-class word-level GED task. No ready-made error-type annotation exists, so the labels were obtained automatically: the authors aligned erroneous sentences with their corrected versions, extracted the edits, and passed them to the ARETA tool. The predicted GED labels are fed to the correction model as additional input. Separately, the authors tested contextual morphological preprocessing (Morph). These systems set a new SOTA on the QALB-2014 and QALB-2015 corpora and provide the first strong benchmark for ZAEBUC. The gains come mainly from recall. For languages such as Ukrainian, the work is valuable because it shows how to create error detection data without manual annotation and how such detection helps correction.

Key points

  • Data: QALB-2014 (native speakers' comments on the Aljazeera website, about 1M words in Train), QALB-2015 (essays by L2 learners of Arabic) and ZAEBUC (essays by native-speaker students). The authors split ZAEBUC themselves in a 70/15/15 ratio, preserving the balance of CEFR levels.
  • GED is considered at three levels of granularity: 43, 13 and 2 classes. The detector is built by fine-tuning CAMeLBERT, so it processes at most 512 subtokens. Insertion errors are not modeled in GED.
  • Quality was evaluated with the M² scorer (F0.5). On average across the test sets, combining Morph and GED raises recall by 1.5 points and lowers precision by about 0.7.
  • An oracle experiment with gold GED labels yielded a much larger gain. Hence the authors' conclusion: GEC is limited primarily by the accuracy of GED prediction, not by the number of classes.
  • Punctuation accounts for about 40% of errors in QALB-2014 and remains difficult for the models. The code, data and models are publicly released: github.com/CAMeL-Lab/arabic-gec.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

24 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 19 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.emnlp-main.396 · arXiv 2305.14734

@inproceedings{alhafni2023advancements,
  title = {{Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation}},
  author = {Alhafni, Bashar and Inoue, Go and Khairallah, Christian and Habash, Nizar},
  booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
  year = {2023},
  doi = {10.18653/v1/2023.emnlp-main.396},
  eprint = {2305.14734},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2023.emnlp-main.396}
}

References

94 references, 18 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish, and Younes Samih. 2021. Pre-training bert on arabic tweets: Practical considerations. ↗ our card
  2. Muhammad Abdul-Mageed, AbdelRahim Elmadany, and El Moatez Billah Nagoudi. 2021. ARBERT & MARBERT: Deep bidirectional transformers for Arabic. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7088–7105, Online. Association for Computational Linguistics. ↗ our card
  3. Abdullah Alfaifi and Eric Atwell. 2012. Arabic learner corpora (alc): a taxonomy of coding errors. In The 8th International Computing Conference in Arabic. ↗
  4. Abdullah Alfaifi, Eric Atwell, and Ghazi Abuhakema. 2013. Error annotation of the Arabic learner corpus. In Language Processing and Knowledge in the Web, pages 14–22. Springer. find
  5. Manar Alkhatib, Azza Abdel Monem, and Khaled Shaalan. 2020. Deep learning for Arabic error detection and correction. ACM Trans. Asian Low-Resour. Lang. Inf. Process., 19(5). ↗
  6. Wissam Antoun, Fady Baly, and Hazem Hajj. 2020. AraBERT: Transformer-based model for Arabic language understanding. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, pages 9–15, Marseille, France. European Language Resource Association. ↗ our card
  7. Hiroki Asano, Tomoya Mizumoto, and Kentaro Inui. 2017. Reference-based metrics can be replaced with reference-less metrics in evaluating grammatical error correction systems. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 343– 348, Taipei, Taiwan. Asian Federation of Natural Language Processing. find our card
  8. Dana Awad. 2013. La ponctuation en Arabe: histoire et règles. etude constrative avec le français et l’anglais. find
  9. Abhijeet Awasthi, Sunita Sarawagi, Rasna Goyal, Sabyasachi Ghosh, and Vihari Piratla. 2019. Parallel iterative edit models for local sequence transduction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4260–4270, Hong Kong, China. Association for Computational Linguistics. ↗
  10. Riadh Belkebir and Nizar Habash. 2021. Automatic error type annotation for Arabic. In Proceedings of the 25th Conference on Computational Natural Language Learning, pages 596–606, Online. Association for Computational Linguistics. ↗
  11. Samuel Bell, Helen Yannakoudakis, and Marek Rei. 2019. Context is key: Grammatical error detection with contextual word representations. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 103– 115, Florence, Italy. Association for Computational Linguistics. find
  12. Fethi Bougares and Houda Bouamor. 2015. UMMU@QALB-2015 shared task: Character and word level SMT pipeline for automatic error correction of Arabic text. In Proceedings of the Second Workshop on Arabic Natural Language Processing, pages 166–172, Beijing, China. Association for Computational Linguistics. find
  13. Christopher Bryant, Mariano Felice, Øistein E. Andersen, and Ted Briscoe. 2019. The BEA-2019 shared task on grammatical error correction. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 52–75, Florence, Italy. Association for Computational Linguistics. find our card
  14. Christopher Bryant, Mariano Felice, and Ted Briscoe. 2017. Automatic annotation and evaluation of error types for grammatical error correction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 793–805, Vancouver, Canada. Association for Computational Linguistics. find our card
  15. Christopher Bryant, Zheng Yuan, Muhammad Reza Qorib, Hannan Cao, Hwee Tou Ng, and Ted Briscoe. 2023. Grammatical error correction: A survey of the state of the art. ↗
  16. Tim Buckwalter. 2004. Issues in Arabic orthography and morphology analysis. In Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages, pages 31–34, Geneva, Switzerland. COLING. find our card
  17. Martin Chodorow, Joel Tetreault, and Na-Rae Han. 2007. Detection of grammatical errors involving prepositions. In Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions, pages 25–30, Prague, Czech Republic. Association for Computational Linguistics. find
  18. Leshem Choshen, Dmitry Nikolaev, Yevgeni Berzak, and Omri Abend. 2020. Classifying syntactic errors in learner language. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 97–107, Online. Association for Computational Linguistics. find our card
  19. Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020. Electra: Pre-training text encoders as discriminators rather than generators. In International Conference on Learning Representations. find
  20. C. o. E. Council of Europe. 2001. Common european framework of reference for languages: learning, teaching, assessment. Cambridge University Press. find
  21. Daniel Dahlmeier and Hwee Tou Ng. 2011. Grammatical error correction with alternating structure optimization. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 915–923, Portland, Oregon, USA. Association for Computational Linguistics. find
  22. Daniel Dahlmeier and Hwee Tou Ng. 2012. Better evaluation for grammatical error correction. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 568–572, Montréal, Canada. Association for Computational Linguistics. find
  23. Daniel Dahlmeier, Hwee Tou Ng, and Siew Mei Wu. 2013. Building a large annotated corpus of learner English: The NUS corpus of learner English. In Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications, pages 22–31, Atlanta, Georgia. Association for Computational Linguistics. find
  24. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics. ↗
  25. Fadhl Eryani, Nizar Habash, Houda Bouamor, and Salam Khalifa. 2020. A spelling correction corpus for multiple Arabic dialects. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4130–4138, Marseille, France. European Language Resources Association. find
  26. Ramy Eskander, Nizar Habash, Owen Rambow, and Nadi Tomeh. 2013. Processing spontaneous orthography. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), pages 585–595, Atlanta, Georgia. find
  27. Tao Fang, Shu Yang, Kaixin Lan, Derek F. Wong, Jinpeng Hu, Lidia S. Chao, and Yue Zhang. 2023. Is chatgpt a highly fluent grammatical error correction system? a comprehensive evaluation. find
  28. Noura Farra, Nadi Tomeh, Alla Rozovskaya, and Nizar Habash. 2014. Generalized character-level spelling error correction. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 161–167, Baltimore, Maryland. Association for Computational Linguistics. ↗
  29. Mariano Felice and Ted Briscoe. 2015. Towards a standard evaluation method for grammatical error detection and correction. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 578–587, Denver, Colorado. Association for Computational Linguistics. find
  30. Mariano Felice, Zheng Yuan, Øistein E. Andersen, Helen Yannakoudakis, and Ekaterina Kochmar. 2014. Grammatical error correction using hybrid systems and type filtering. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task, pages 15–24, Baltimore, Maryland. Association for Computational Linguistics. find
  31. Charles F Ferguson. 1959. Diglossia. Word, 15(2):325– 340. find
  32. Roman Grundkiewicz, Marcin Junczys-Dowmunt, and Kenneth Heafield. 2019. Neural grammatical error correction systems with unsupervised pre-training on synthetic data. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 252–263, Florence, Italy. Association for Computational Linguistics. find
  33. Nizar Habash, Mona Diab, and Owen Rambow. 2012. Conventional orthography for dialectal Arabic. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12), pages 711–718, Istanbul, Turkey. European Language Resources Association (ELRA). Nizar Habash, Salam Khalifa, Fadhl Eryani, Owen Rambow, Dana Abdulrahim, Alexander Erdmann, Reem Faraj, Wajdi Zaghouani, Houda Bouamor, Nasser Zalmout, Sara Hassan, Faisal Al shargi, Sakhar Alkhereyf, Basma Abdulkareem, Ramy Eskander, Mohammad Salameh, and Hind Saddiki. 2018. Unified Guidelines and Resources for Arabic Dialect Orthography. In Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. find our card
  34. Nizar Habash and David Palfreyman. 2022. ZAEBUC: An annotated Arabic-English bilingual writer corpus. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 79–88, Marseille, France. European Language Resources Association. find
  35. Nizar Habash, Owen Rambow, Mona Diab, and Reem Kanjawi-Faraj. 2008. Guidelines for Annotation of Arabic Dialectness. In Proceedings of the Workshop on HLT & NLP within the Arabic World, Marrakech, Morocco. find
  36. Nizar Habash and Ryan Roth. 2011. Using deep morphology to improve automatic error detection in Arabic handwriting recognition. In Proceedings of the Conference of the Association for Computational Linguistics (ACL), pages 875–884, Portland, Oregon, USA. find
  37. Nizar Habash, Abdelhadi Soudi, and Tim Buckwalter. 2007. On Arabic Transliteration. In A. van den Bosch and A. Soudi, editors, Arabic Computational Morphology: Knowledge-based and Empirical Methods, pages 15–22. Springer, Netherlands. ↗
  38. Nizar Y Habash. 2010. Introduction to Arabic natural language processing, volume 3. Morgan & Claypool Publishers. find
  39. Go Inoue, Bashar Alhafni, Nurpeiis Baimukan, Houda Bouamor, and Nizar Habash. 2021. The interplay of variant, size, and task type in Arabic pre-trained language models. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 92– 104, Kyiv, Ukraine (Virtual). Association for Computational Linguistics. find our card
  40. Go Inoue, Salam Khalifa, and Nizar Habash. 2022. Morphosyntactic tagging with pre-trained language models for Arabic and its dialects. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1708–1719, Dublin, Ireland. Association for Computational Linguistics. find
  41. Marcin Junczys-Dowmunt and Roman Grundkiewicz. 2014. The AMU system in the CoNLL-2014 shared task: Grammatical error correction by data-intensive and feature-rich statistical machine translation. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task, pages 25–33, Baltimore, Maryland. Association for Computational Linguistics. find
  42. Marcin Junczys-Dowmunt and Roman Grundkiewicz. 2016. Phrase-based machine translation is state-of-the-art for automatic grammatical error correction. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1546–1556, Austin, Texas. Association for Computational Linguistics. find
  43. Marcin Junczys-Dowmunt, Roman Grundkiewicz, Shubha Guha, and Kenneth Heafield. 2018. Approaching neural grammatical error correction as a low-resource machine translation task. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 595–606, New Orleans, Louisiana. Association for Computational Linguistics. find
  44. Moussa Kamal Eddine, Nadi Tomeh, Nizar Habash, Joseph Le Roux, and Michalis Vazirgiannis. 2022. AraBART: a pretrained Arabic sequence-to-sequence model for abstractive summarization. In Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP), pages 31–42, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics. find
  45. Masahiro Kaneko and Mamoru Komachi. 2019. Multi-head multi-layer attention to deep language representations for grammatical error detection. ↗
  46. Satoru Katsumata and Mamoru Komachi. 2020. Stronger baselines for grammatical error correction using a pretrained encoder-decoder model. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 827–832, Suzhou, China. Association for Computational Linguistics. ↗
  47. Ekaterina Kochmar, Øistein Andersen, and Ted Briscoe. 2012. HOO 2012 error recognition and correction shared task: Cambridge University submission report. In Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, pages 242–250, Montréal, Canada. Association for Computational Linguistics. find
  48. Wuwei Lan, Yang Chen, Wei Xu, and Alan Ritter. 2020. An empirical study of pre-trained transformers for Arabic information extraction. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4727–4734, Online. Association for Computational Linguistics. find
  49. V. I. Levenshtein. 1966. Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady, 10:707. find
  50. Zuchao Li, Kevin Parnow, and Hai Zhao. 2022. Incorporating rich syntax information in grammatical error correction. Information Processing & Management, 59(3):102891. ↗
  51. Nora Madi and Hend Al-Khalifa. 2020. Error detection for Arabic text using neural sequence labeling. Applied Sciences, 10(15). ↗
  52. Koki Maeda, Masahiro Kaneko, and Naoaki Okazaki. 2022. IMPARA: Impact-based metric for GEC using parallel data. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3578–3588, Gyeongju, Republic of Korea. International Committee on Computational Linguistics. find
  53. Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn. 2022. EdiT5: Semi-autoregressive text editing with t5 warm-start. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2126–2138, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. find
  54. Jonathan Mallinson, Aliaksei Severyn, Eric Malmi, and Guillermo Garrido. 2020. FELIX: Flexible text editing through tagging and insertion. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1244–1255, Online. Association for Computational Linguistics. find
  55. Eric Malmi, Sebastian Krause, Sascha Rothe, Daniil Mirylenka, and Aliaksei Severyn. 2019. Encode, tag, realize: High-precision text editing. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5054–5065, Hong Kong, China. Association for Computational Linguistics. ↗ our card
  56. Stuart Mesham, Christopher Bryant, Marek Rei, and Zheng Yuan. 2023. An extended sequence tagging vocabulary for grammatical error correction. find
  57. Rada Mihalcea and Ted Pedersen. 2003. An evaluation exercise for word alignment. In Proceedings of the HLT-NAACL 2003 Workshop on Building and Using Parallel Texts: Data Driven Machine Translation and Beyond, pages 1–10. find our card
  58. Behrang Mohit, Alla Rozovskaya, Nizar Habash, Wajdi Zaghouani, and Ossama Obeid. 2014. The first QALB shared task on automatic text correction for Arabic. In Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP), pages 39–47, Doha, Qatar. Association for Computational Linguistics. find
  59. El Moatez Billah Nagoudi, AbdelRahim Elmadany, and Muhammad Abdul-Mageed. 2022. AraT5: Text-to-text transformers for Arabic language generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 628–647, Dublin, Ireland. Association for Computational Linguistics. find
  60. Courtney Napoles, Keisuke Sakaguchi, Matt Post, and Joel Tetreault. 2015. Ground truth for grammatical error correction metrics. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 588–593, Beijing, China. Association for Computational Linguistics. find
  61. Courtney Napoles, Keisuke Sakaguchi, and Joel Tetreault. 2016. There’s no comparison: Reference-less evaluation metrics in grammatical error correction. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2109–2115, Austin, Texas. Association for Computational Linguistics. find
  62. Michael Nawar. 2015. CUFE@QALB-2015 shared task: Arabic error correction system. In Proceedings of the Second Workshop on Arabic Natural Language Processing, pages 133–137, Beijing, China. Association for Computational Linguistics. find
  63. Hwee Tou Ng, Siew Mei Wu, Ted Briscoe, Christian Hadiwinoto, Raymond Hendy Susanto, and Christopher Bryant. 2014. The CoNLL-2014 shared task on grammatical error correction. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task, pages 1–14, Baltimore, Maryland. Association for Computational Linguistics. find
  64. Hwee Tou Ng, Siew Mei Wu, Yuanbin Wu, Christian Hadiwinoto, and Joel Tetreault. 2013. The CoNLL-2013 shared task on grammatical error correction. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task, pages 1–12, Sofia, Bulgaria. Association for Computational Linguistics. find
  65. Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash. 2020. CAMeL tools: An open source python toolkit for Arabic natural language processing. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 7022–7032, Marseille, France. European Language Resources Association. find our card
  66. Franz Josef Och and Hermann Ney. 2003. A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1):19–51. ↗
  67. Kostiantyn Omelianchuk, Vitaliy Atrasevych, Artem Chernodub, and Oleksandr Skurzhanskyi. 2020. GECToR – grammatical error correction: Tag, not rewrite. In Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 163–170, Seattle, WA, USA → Online. Association for Computational Linguistics. find
  68. Marek Rei and Helen Yannakoudakis. 2016. Compositional sequence labeling models for error detection in learner writing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1181–1191, Berlin, Germany. Association for Computational Linguistics. find
  69. Sascha Rothe, Jonathan Mallinson, Eric Malmi, Sebastian Krause, and Aliaksei Severyn. 2021. A simple recipe for multilingual grammatical error correction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 702–707, Online. Association for Computational Linguistics. find
  70. Alla Rozovskaya, Houda Bouamor, Nizar Habash, Wajdi Zaghouani, Ossama Obeid, and Behrang Mohit. 2015. The second QALB shared task on automatic text correction for Arabic. In Proceedings of the Second Workshop on Arabic Natural Language Processing, pages 26–35, Beijing, China. Association for Computational Linguistics. find
  71. Alla Rozovskaya, Nizar Habash, Ramy Eskander, Noura Farra, and Wael Salloum. 2014. The Columbia system in the QALB-2014 shared task on Arabic error correction. In Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP), pages 160–164, Doha, Qatar. Association for Computational Linguistics. ↗
  72. Alla Rozovskaya and Dan Roth. 2013. Joint learning and inference for grammatical error correction. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 791– 802, Seattle, Washington, USA. Association for Computational Linguistics. find
  73. Ali Safaya, Moutasem Abdullatif, and Deniz Yuret. 2020. KUISAIL at SemEval-2020 task 12: BERT-CNN for offensive speech identification in social media. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2054–2059, Barcelona (online). International Committee for Computational Linguistics. find
  74. Aiman Solyman, Zhenyu Wang, Qian Tao, Arafat Abdulgader Mohammed Elhag, Rui Zhang, and Zeinab Mahmoud. 2022. Automatic Arabic grammatical error correction based on expectation-maximization routing and target-bidirectional agreement. Knowledge-Based Systems, 241:108180. ↗
  75. Aiman Solyman, Marco Zappatore, Wang Zhenyu, Zeinab Mahmoud, Ali Alfatemi, Ashraf Osman Ibrahim, and Lubna Abdelkareim Gabralla. 2023. Optimizing the impact of data augmentation for low-resource grammatical error correction. Journal of King Saud University - Computer and Information Sciences, 35(6):101572. ↗
  76. Aiman Solyman, Wang Zhenyu, Tao Qian, Arafat Abdulgader Mohammed Elhag, Muhammad Toseef, and Zeinab Aleibeid. 2021. Synthetic data with neural machine translation for automatic correction in Arabic grammar. Egyptian Informatics Journal, 22(3):303–315. ↗
  77. Felix Stahlberg and Shankar Kumar. 2020. Seq2Edits: Sequence transduction using span-level edit operations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5147–5159, Online. Association for Computational Linguistics. find
  78. Milan Straka, Jakub Náplava, and Jana Straková. 2021. Character transformations for non-autoregressive GEC tagging. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 417–422, Online. Association for Computational Linguistics. ↗
  79. Dima Taji, Jamila El Gizuli, and Nizar Habash. 2018. An Arabic dependency treebank in the travel domain. In Proceedings of the Workshop on Open-Source Arabic Corpora and Processing Tools (OS-ACT), Miyazaki, Japan. ↗ our card
  80. Joel R. Tetreault and Martin Chodorow. 2008. The ups and downs of preposition error detection in ESL writing. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 865–872, Manchester, UK. Coling 2008 Organizing Committee. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc. evaluation. In The 2nd Workshop on Arabic Corpora and Processing Tools 2016 Theme: Social Media, page 22. find
  81. Daniel Watson, Nasser Zalmout, and Nizar Habash. 2018. Utilizing character and word embeddings for text normalization with sequence-to-sequence models. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 837–843, Brussels, Belgium. Association for Computational Linguistics. ↗
  82. Wajdi Zaghouani, Nizar Habash, Houda Bouamor, Alla Rozovskaya, Behrang Mohit, Abeer Heider, and Kemal Oflazer. 2015. Correction annotation for non-native arabic texts: Guidelines and corpus. In Proceedings of the Linguistic Annotation Workshop (LAW), pages 129–139. find
  83. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019. Huggingface’s transformers: State-of-the-art natural language processing. ArXiv, abs/1910.03771. ↗ our card
  84. Ziang Xie, Anand Avati, Naveen Arivazhagan, Dan Jurafsky, and Andrew Y. Ng. 2016. Neural language correction with character-based attention. ↗
  85. Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc. ↗ our card
  86. Helen Yannakoudakis, Ted Briscoe, and Ben Medlock. 2011. A new dataset and method for automatically grading ESOL texts. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 180–189, Portland, Oregon, USA. Association for Computational Linguistics. find
  87. Zheng Yuan and Ted Briscoe. 2016. Grammatical error correction using neural machine translation. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 380–386, San Diego, California. Association for Computational Linguistics. find
  88. Wajdi Zaghouani, Behrang Mohit, Nizar Habash, Ossama Obeid, Nadi Tomeh, Alla Rozovskaya, Noura Farra, Sarah Alkuhlani, and Kemal Oflazer. 2014. Large scale Arabic error annotation: Guidelines and framework. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), Reykjavik, Iceland. European Language Resources Association (ELRA). ↗ our card
  89. Yue Zhang, Bo Zhang, Zhenghua Li, Zuyi Bao, Chen Li, and Min Zhang. 2022. SynGEC: Syntax-enhanced grammatical error correction with a tailored GEC-oriented parser. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2518–2531, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. find our card
  90. Wei Zhao, Liang Wang, Kewei Shen, Ruoyu Jia, and Jingming Liu. 2019. Improving grammatical error correction via pre-training a copy-augmented architecture with unlabeled data. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 156–165, Minneapolis, Minnesota. Association for Computational Linguistics. ↗
  91. Zheng Yuan and Christopher Bryant. 2021. Document-level grammatical error correction. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pages 75–84, Online. Association for Computational Linguistics. find
  92. Zheng Yuan, Felix Stahlberg, Marek Rei, Bill Byrne, and Helen Yannakoudakis. 2019. Neural and FST-based approaches to grammatical error correction. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 228–239, Florence, Italy. Association for Computational Linguistics. find
  93. Zheng Yuan, Shiva Taslimipoor, Christopher Davis, and Christopher Bryant. 2021. Multi-class grammatical error detection for correction: A tale of two systems. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8722–8736, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. find
  94. Wajdi Zaghouani and Dana Awad. 2016. Toward an Arabic punctuated corpus: Annotation guidelines and find

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/alhafni-2023-advancements-arabic-grammatical-error-detection/