DialectNLU at NADI 2023 Shared Task: Transformer Based Multitask Approach Jointly Integrating Dialect and Machine Translation Tasks in Arabic
Veeramani, Hariram, Thapa, Surendrabikram, and Naseem, Usman (2023) DialectNLU at NADI 2023 Shared Task: Transformer Based Multitask Approach Jointly Integrating Dialect and Machine Translation Tasks in Arabic. In: Proceedings of the 1st Arabic Natural Language Processing Conference. pp. 614-619. From: ArabicNLP 2023: 1st Arabic Natural Language Processing Conference, 7 December 2023, Singapore.
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Abstract
With approximately 400 million speakers worldwide, Arabic ranks as the fifth most-spoken language globally, necessitating advancements in natural language processing. This paper describes the approaches employed for the subtasks outlined in the Nuanced Arabic Dialect Identification (NADI) task at EMNLP 2023. We employ an ensemble of two Arabic language models for the first subtask involving closed country-level dialect identification classification. Similarly, for the second subtask, focused on closed dialect to Modern Standard Arabic (MSA) machine translation, our approach combines sequence-to-sequence models trained on an Arabic-specific dataset. Our team ranks 10th and 3rd on subtask 1 and subtask 2, respectively.
| Item ID: | 82156 |
|---|---|
| Item Type: | Conference Item (Research - E1) |
| ISBN: | 9781959429272 |
| Copyright Information: | Creative Commons 4.0 BY (Attribution) license. |
| Date Deposited: | 13 Mar 2024 01:40 |
| FoR Codes: | 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 100% |
| SEO Codes: | 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 100% |
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