| Challenge: | Multidialectal Arabic POS tagging is challenging due to the morphological richness and high variability among dialects. |
| Approach: | They propose an active learning approach for multidialectal Arabic POS tagging . they annotate approximately 15,000 tokens, reducing the annotation requirement by about 2,000 tokens . |
| Outcome: | The proposed approach achieves 97.6% accuracy on the Emirati corpus. |
Similar Papers
Multi-Dialect Arabic POS Tagging: A CRF Approach (L18-1)
Copied to clipboard
Kareem Darwish, Hamdy Mubarak, Ahmed Abdelali, Mohamed Eldesouki, Younes Samih, Randah Alharbi, Mohammed Attia, Walid Magdy, Laura Kallmeyer
| Challenge: | Existing work on dialectal POS tagging is rather scant with POS tags for most dialects being nonexistent or of limited availability. |
| Approach: | They propose a dataset of POS-tagged Arabic tweets in four major dialects and a tagging guideline for each dialect. |
| Outcome: | The proposed model can tag four different dialects with an average accuracy of 89.3%. |
Reducing Confusion in Active Learning for Part-Of-Speech Tagging (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing algorithms for annotating parts of speech are not optimal for all languages. |
| Approach: | They propose to use a data selection algorithm to select useful training samples to minimize annotation cost. |
| Outcome: | The proposed strategy outperforms existing strategies on six typologically diverse languages. |
Part-of-Speech Tagging for Arabic Gulf Dialect Using Bi-LSTM (L18-1)
Copied to clipboard
| Challenge: | Part-of-speech (POS) tagging is one of the most important building blocks in many natural language processing (NLP) applications. |
| Approach: | They propose to use a POS tagger for Arabic Gulf dialect to improve POS tagging accuracy. |
| Outcome: | The proposed POS tagger improves POS tagging accuracy for the Arabic Gulf dialect from 75% accuracy to 91% accuracy using a bi-LSTM labeler. |
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian (2020.lrec-1)
Copied to clipboard
Seyed Arad Ashrafi Asli, Behnam Sabeti, Zahra Majdabadi, Preni Golazizian, Reza Fahmi, Omid Momenzadeh
| Challenge: | Existing deep learning approaches require huge amounts of data to be trained properly. |
| Approach: | They propose to use Persian as a model to choose the samples for annotation instead of labeling the whole dataset. |
| Outcome: | The proposed models achieve the baseline performance with a significantly lower amount of labeled data. |
EMAD: A Bridge Tagset for Unifying Arabic POS Annotations (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing tagsets for Arabic are difficult to combine due to the diversity of their features. |
| Approach: | They propose an Arabic Extended Morphological Analysis and Disambiguation Tagset which facilitates conversion and unification of Arabic tagsets. |
| Outcome: | The proposed tagset facilitates conversion and unification of different tagsetes used to annotate Arabic corpora. |
Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling (P19-1)
Copied to clipboard
| Challenge: | Morphological tagging is challenging for morphologically rich languages due to the large combined target space and the need for more training data to minimize model sparsity. |
| Approach: | They propose to use multitask learning and adversarial training to address morphological richness and dialectal variations in the context of full morphology. |
| Outcome: | The proposed model achieves state-of-the-art for two dialectal variants: Modern Standard Arabic (high-resource “dialect”) and Egyptian Arabic (low-resourced dialect). |
On Efficiently Acquiring Annotations for Multilingual Models (2022.acl-short)
Copied to clipboard
| Challenge: | a recent study shows that joint learning across multiple languages performs better than the aforementioned approaches . traditional approaches to support NLP tasks require a lot of annotations to perform . a new approach is to train a model for each language with annotation budget divided equally among them . |
| Approach: | They propose a method for joint learning across multiple languages using a single model . they show that active learning provides additional, complementary benefits . |
| Outcome: | The proposed method outperforms other models on a diverse set of tasks . it can arbitrate its annotation budget to query languages it is less certain on . |
Deep Active Learning for Morphophonological Processing (2023.acl-short)
Copied to clipboard
Seyed Morteza Mirbostani, Yasaman Boreshban, Salam Khalifa, SeyedAbolghasem Mirroshandel, Owen Rambow
| Challenge: | Existing deep learning models for morphological processing require a large amount of annotated data. |
| Approach: | They propose a deep active learning method that uses only informative samples to reduce the need for annotated data. |
| Outcome: | The proposed method achieves the same results as the state-of-the-art model on Egyptian Arabic with only about 30% of annotated data. |
Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects (2022.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained morphosyntactic tagging models outperform existing systems in Modern Standard Arabic and all the Arabic dialects studied. |
| Approach: | They present results on morphosyntactic tagging across different varieties of Arabic using pre-trained transformer language models. |
| Outcome: | The proposed models outperform existing systems in Modern Standard Arabic, 2.8% in Gulf, 1.6% in Egyptian, and 8.3% in Levantine. |
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates (2021.eacl-main)
Copied to clipboard
Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V. Dylov, Alexander Panchenko
| Challenge: | Annotating training data for sequence tagging of texts is usually very time-consuming . active learning can help to reduce the amount of annotation required to train a good model by multiple times . |
| Approach: | They are the first to thoroughly investigate active learning and transfer learning for natural language processing . they propose to combine active learning with active learning to improve model acquisition . |
| Outcome: | The proposed combination of active learning and Bayesian uncertainty estimation improves performance and reduces obstacles for applying it in practice. |