Confidence Learning from Noisy Labels for Arabic Dialect Identification

Zainab Alhakeem; Hong Goo Kang

doi:10.1109/ITC-CSCC52171.2021.9741073

Confidence Learning from Noisy Labels for Arabic Dialect Identification

Zainab Alhakeem, Hong Goo Kang

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we propose a new deep learning network for Arabic Dialect Identification (ADI) that addresses the incorrect label problem using confidence information. The recently released dataset for the MGB-5 ADI challenge includes a small amount of verified labels but a large amount of noisy labels, which makes the ADI task very challenging. We propose a confidence learning network (CLN) that utilizes a multi-task learning strategy to handle confidence information by leveraging the verified and noisy label sets. The proposed CLN employs a confidence refinement module using a Gumbel-softmax sampler that generates representations with more discriminative capabilities. Experimental results demonstrate that the proposed CLN model shows higher classification accuracy than conventional state-of-the-art ADI systems.

Original language	English
Title of host publication	2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665435536
DOIs	https://doi.org/10.1109/ITC-CSCC52171.2021.9741073
Publication status	Published - 2021 Jun 27
Event	36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021 - Jeju, Korea, Republic of Duration: 2021 Jun 27 → 2021 Jun 30

Publication series

Name	2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021
Volume	2021-January

Conference

Conference	36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021
Country/Territory	Korea, Republic of
City	Jeju
Period	21/6/27 → 21/6/30

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported in part by Artificial Intelligence Graduate School Program under Grant 2020-0-01361.

Publisher Copyright:
© 2021 IEEE.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Hardware and Architecture
Information Systems
Electrical and Electronic Engineering

Access to Document

10.1109/ITC-CSCC52171.2021.9741073

Cite this

Alhakeem, Z., & Kang, H. G. (2021). Confidence Learning from Noisy Labels for Arabic Dialect Identification. In 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021 (2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021; Vol. 2021-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITC-CSCC52171.2021.9741073

Alhakeem, Zainab ; Kang, Hong Goo. / Confidence Learning from Noisy Labels for Arabic Dialect Identification. 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021).

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title = "Confidence Learning from Noisy Labels for Arabic Dialect Identification",

abstract = "In this paper, we propose a new deep learning network for Arabic Dialect Identification (ADI) that addresses the incorrect label problem using confidence information. The recently released dataset for the MGB-5 ADI challenge includes a small amount of verified labels but a large amount of noisy labels, which makes the ADI task very challenging. We propose a confidence learning network (CLN) that utilizes a multi-task learning strategy to handle confidence information by leveraging the verified and noisy label sets. The proposed CLN employs a confidence refinement module using a Gumbel-softmax sampler that generates representations with more discriminative capabilities. Experimental results demonstrate that the proposed CLN model shows higher classification accuracy than conventional state-of-the-art ADI systems.",

author = "Zainab Alhakeem and Kang, {Hong Goo}",

note = "Funding Information: ACKNOWLEDGMENT This work was supported in part by Artificial Intelligence Graduate School Program under Grant 2020-0-01361. Publisher Copyright: {\textcopyright} 2021 IEEE.; 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021 ; Conference date: 27-06-2021 Through 30-06-2021",

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doi = "10.1109/ITC-CSCC52171.2021.9741073",

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Alhakeem, Z & Kang, HG 2021, Confidence Learning from Noisy Labels for Arabic Dialect Identification. in 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021. 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021, vol. 2021-January, Institute of Electrical and Electronics Engineers Inc., 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021, Jeju, Korea, Republic of, 21/6/27. https://doi.org/10.1109/ITC-CSCC52171.2021.9741073

Confidence Learning from Noisy Labels for Arabic Dialect Identification. / Alhakeem, Zainab; Kang, Hong Goo.

2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021; Vol. 2021-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - In this paper, we propose a new deep learning network for Arabic Dialect Identification (ADI) that addresses the incorrect label problem using confidence information. The recently released dataset for the MGB-5 ADI challenge includes a small amount of verified labels but a large amount of noisy labels, which makes the ADI task very challenging. We propose a confidence learning network (CLN) that utilizes a multi-task learning strategy to handle confidence information by leveraging the verified and noisy label sets. The proposed CLN employs a confidence refinement module using a Gumbel-softmax sampler that generates representations with more discriminative capabilities. Experimental results demonstrate that the proposed CLN model shows higher classification accuracy than conventional state-of-the-art ADI systems.

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Alhakeem Z, Kang HG. Confidence Learning from Noisy Labels for Arabic Dialect Identification. In 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021). doi: 10.1109/ITC-CSCC52171.2021.9741073

Confidence Learning from Noisy Labels for Arabic Dialect Identification

Abstract

Publication series

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Bibliographical note

All Science Journal Classification (ASJC) codes

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