Papers by Kemal Oflazer
The MADAR Arabic Dialect Corpus and Lexicon (L18-1)
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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. |
MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction (L18-1)
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| Challenge: | Standard Arabic morphology is rich, but Arabic dialects introduce more complexity. |
| Approach: | They propose a joint morphological annotation and spelling correction system for Arabic texts . they propose morphology tools that can be used to help with productivity . |
| Outcome: | The proposed system is based on a standard and dialectal Arabic text. |
Counting the Bugs in ChatGPT’s Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model (2023.emnlp-main)
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Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban, Anna Cai, Ritam Dutt, Amey Hengle, Anubha Kabra, Atharva Kulkarni, Abhishek Vijayakumar, Haofei Yu, Hinrich Schuetze, Kemal Oflazer, David Mortensen
| Challenge: | Existing studies on large language models (LLMs) ignore the remarkable ability of humans to generalize and focus only on English. |
| Approach: | They conduct the first rigorous analysis of the morphological capabilities of ChatGPT in four typologically varied languages. |
| Outcome: | The proposed model massively underperforms purpose-built systems, particularly in English. |