natural-language-processing-11

Dialect Identification

3260 papers • 126 benchmarks • 313 datasets

Dialectal Arabic Identification

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in dialect-identification-22

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Dataset

Best Model

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No benchmarks available.

Libraries

i

Use these libraries to find dialect-identification-22 models and implementations

Datasets

ArSarcasm-v2

ArSarcasm

FreCDo

Subtasks

No subtasks available.

Most implemented papers

AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset

Mohamed Seghir Hadj Ameur, H. Aliane•Thu May 06 2021

This paper releases "AraCOVID19-MFH" a manually annotated multi-label Arabic COVID-19 fake news and hate speech detection dataset that contains 10,828 Arabic tweets annotated with 10 different labels.

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GlotLID: Language Identification for Low-Resource Languages

Hinrich Schütze, Amir Hossein Kargaran, Ayyoob Imani, François Yvon•Mon Oct 23 2023

GlotLID-M is published, an LID model that satisfies the desiderata of wide coverage, reliability and efficiency, and it identifies 1665 languages, a large increase in coverage compared to prior work.

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Automatic Dialect Detection in Arabic Broadcast Speech

S. Renals, James R. Glass, Ahmed M. Ali, P. Bell, N. Dehak, P. Cardinal, Sameer Khurana, S. Yella•Tue Sep 22 2015

This work investigates different approaches for dialect identification in Arabic broadcast speech, using phonetic, lexical features obtained from a speech recognition system, and acoustic features using the i-vector framework, and combined these features using a multi-class Support Vector Machine (SVM).

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Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource

Walter Daelemans, Chris Emmery, Stéphan Tulkens•Thu Jun 30 2016

This paper demonstrates the performance of multiple types of embeddings, created with both count and prediction-based architectures on a variety of corpora, in two language-specific tasks: relation evaluation, and dialect identification.

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A Character-level Convolutional Neural Network for Distinguishing Similar Languages and Dialects

James R. Glass, Yonatan Belinkov•Fri Sep 23 2016

This paper describes the submission to the DSL 2016 shared-task, which included two sub-tasks: one on discriminating similar languages and one on identifying Arabic dialects, and develops a character-level neural network for this task.

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CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing

Nizar Habash, Salam Khalifa, Ossama Obeid, Nasser Zalmout, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann•Thu Apr 30 2020

The design of CAMeL Tools is described and the functionalities it provides are described, including utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and Sentiment Analysis.

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Multi-dialect Arabic BERT for Country-level Dialect Identification

Ibraheem Tuffaha, Haitham Seelawi, Wael Farhan, Hussein T. Al-Natsheh, Bashar Talafha, Mohammad Ali, Muhy Eddin Za’ter, Mostafa Samir•Thu Jul 09 2020

This paper presents the experiments conducted, and the models developed by the competing team, Mawdoo3 AI, along the way to achieving the winning solution to subtask 1 of the Nuanced Arabic Dialect Identification (NADI) shared task.

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Speech recognition challenge in the wild: Arabic MGB-3

S. Renals, Ahmed M. Ali, S. Vogel•Wed Sep 20 2017

The Arabic MGB-Challenge comprised two tasks: speech transcription and Arabic dialect identification, introduced this year in order to distinguish between four major Arabic dialects — Egyptian, Levantine, North African, Gulf, as well as Modern Standard Arabic.

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The unreasonable effectiveness of machine learning in Moldavian versus Romanian dialect identification

Radu Tudor Ionescu, Mihaela Guaman•Wed Jul 29 2020

A subjective evaluation by human annotators shows that humans attain much lower accuracy rates compared with ML models, and experiments show that ML models can accurately identify the dialects, even at the sentence level and across different domains (news articles vs. tweets).

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Adding a benchmark result helps the community track progress.