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NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task
ARABICNLP, 2023We describe the findings of the fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023). The objective of NADI is to help advance state-of-the-art Arabic NLP by creating opportunities for teams of researchers to collaboratively compete under
M. Abdul-Mageed +5 more
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Quantifying the Dialect Gap and its Correlates Across Languages
Conference on Empirical Methods in Natural Language Processing, 2023Historically, researchers and consumers have noticed a decrease in quality when applying NLP tools to minority variants of languages (i.e. Puerto Rican Spanish or Swiss German), but studies exploring this have been limited to a select few languages ...
Anjali Kantharuban +2 more
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Hierarchical Aggregation of Dialectal Data for Arabic Dialect Identification
International Conference on Language Resources and Evaluation, 2022Arabic is a collection of dialectal variants that are historically related but significantly different. These differences can be seen across regions, countries, and even cities in the same countries.
Nurpeiis Baimukan +2 more
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Does dialect similarity add value to banks? Evidence from China
Journal of Banking and Finance, 2019This study examines the value of language, as an important dimension of culture, to banks. Based on a unique hand-collected dataset of Chinese commercial banks and a county-level dialect dataset, we find that a higher degree of dialect similarity between
Hao Zhang
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Dialect areas and dialect continua
Language Variation and Change, 2001The organizing concept behind dialect variation is still seen predominantly as the areas within which similar varieties are spoken. The opposing view—that dialects are organized in a continuum without sharp boundaries—is likewise popular. This article introduces a new element into the discussion, which is the opportunity to view dialectal ...
Heeringa, Wilbert, Nerbonne, J.
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Multi-Dialect Speech Recognition in English Using Attention on Ensemble of Experts
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021In the presence of a wide variety of dialects, training dialect-specific models for each dialect is a demanding task. Previous studies have explored training a single model that is robust across multiple dialects.
Amit Das, Kshitiz Kumar, Jian Wu
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ADI17: A Fine-Grained Arabic Dialect Identification Dataset
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020In this paper, we describe a method to collect dialectal speech from YouTube videos to create a large-scale Dialect Identification (DID) dataset. Using this method, we collected dialectal Arabic from known YouTube channels from 17 Arabic speaking ...
Suwon Shon +4 more
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