Papers by Fadhl Eryani
The MADAR Arabic Dialect Corpus and Lexicon (L18-1)
Copied to clipboard
Houda Bouamor, Nizar Habash, Mohammad Salameh, Wajdi Zaghouani, Owen Rambow, Dana Abdulrahim, Ossama Obeid, Salam Khalifa, Fadhl Eryani, Alexander Erdmann, Kemal Oflazer
| Challenge: | Using a corpus of 25 Arabic city dialects and a lexicon of 1,045 concepts, we study 25 cities in a travel domain . focus on cities opens new avenues for research from dialectology to dialect identification and machine translation. |
| Approach: | They present two Arabic language resources that are part of the Multi Arabic Dialect Applications and Resources project. |
| Outcome: | The proposed resources are the first of their kind in terms of their coverage and fine granularity. |
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus (2024.lrec-main)
Copied to clipboard
| Challenge: | a corpus of multilingual Arabic-English speech is presented in a new paper . a major bottleneck is the lack of data needed for training NLP models . |
| Approach: | They propose a multilingual multidialectal Arabic-English speech corpus with a set of guidelines for automatic speech recognition. |
| Outcome: | The proposed corpus includes two languages with Arabic and English spoken in multiple variants and Arabic and Arabic with various accents. |
Cross-Lingual Transfer from Related Languages: Treating Low-Resource Maltese as Multilingual Code-Switching (2024.eacl-long)
Copied to clipboard
| Challenge: | Multilingual models exhibit impressive cross-lingual transfer capabilities on unseen languages, but performance is impacted when there is a script disparity with the languages used in the model’s pre-training data. |
| Approach: | They propose a novel method to align a resource-rich language's script with a target language and train a classifier that can make informed decisions regarding the appropriate processing of each token. |
| Outcome: | The proposed model can be used to transfer a language's scripts across multiple languages, but it is suboptimal for mixed languages, where only a subset benefits while the rest is impeded. |
Unified Guidelines and Resources for Arabic Dialect Orthography (L18-1)
Copied to clipboard
Nizar Habash, Fadhl Eryani, Salam Khalifa, 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, Hind Saddiki
| Challenge: | Existing efforts to conventionalize the dialectal orthography of Arabic have focused on specific dialects and made ad hoc decisions. |
| Approach: | They propose a set of guidelines and meta-guidelines for conventional orthography of Arabic dialects . they apply them to 28 Arab city dialects from Rabat to Muscat . |
| Outcome: | The proposed guidelines and resources are being used by three large Arabic dialect processing projects in three universities. |
A Morphologically Annotated Corpus of Emirati Arabic (L18-1)
Copied to clipboard
| Challenge: | Emirati Arabic corpus is first large-scale morphologically manually annotated corpus . resources for dialectal Arabic NLP tasks are still lacking compared to those for modern standard Arabic (MSA). |
| Approach: | They propose to annotate a large-scale corpus of Emirati Arabic using a morphologically manually annotated corpus from eight Gumar novels . they discuss the guidelines for each part of the annotation components, and the annotation interface they use. |
| Outcome: | The annotated corpus includes about 200,000 words from eight Gumar novels in the Emirati Arabic variety. |
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing (2020.lrec-1)
Copied to clipboard
Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, Nizar Habash
| Challenge: | CAMeL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Approach: | They present CAMeL Tools, an open-source Python toolkit for Arabic natural language processing . CAMeleL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Outcome: | The proposed tools are based on CAMeL Tools, an open-source Python toolkit for Arabic natural language processing. |
A Spelling Correction Corpus for Multiple Arabic Dialects (2020.lrec-1)
Copied to clipboard
| Challenge: | Arabic dialects are non-standard varieties of Arabic commonly spoken across the Arab world, but lack standard orthographies. |
| Approach: | They present a corpus of 10,000 sentences from five Arabic city dialects represented in the Conventional Orthography for Dialectal Arabic (CODA) they use a bootstrapping technique to speed up annotation and compare similarity between dialects before and after CODA annotation. |
| Outcome: | The proposed method speeds up the annotation process and shows similarity between the dialects before and after CODA annotation. |