Crowdsourcing speech and language data for resource-poor languages

Hamdy Mubarak

doi:10.1007/978-3-319-64861-3_41

Crowdsourcing speech and language data for resource-poor languages

Hamdy Mubarak^*

^*Corresponding author for this work

Qatar Computing Research Institute

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

1 Citation (Scopus)

Abstract

In this paper, we present benefits of using crowdsourcing to build speech and language resources for different annotation tasks for dialectal Arabic as an example of resource-poor languages. We show recommendations for job design and quality control that allow us to build high quality data for variety of tasks. Most of these recommendations are language-independent and can be applied to other languages as well. We summarize lessons learned from experiments in data acquisition tasks, such as image annotation (transcription of Arabic historical documents), machine translation (translation from English to Hindi), speech annotation (transcription of dialectal Arabic audio files), text annotation (conversion from dialectal Arabic to Modern Standard Arabic (MSA)), and text classification (annotation of offensive language on Arabic social media, and classification of questions on Arabic medical web forums).

Original language	English
Title of host publication	Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017
Editors	Mohamed F. Tolba, Tarek Gaber, Khaled Shaalan, Aboul Ella Hassanien
Publisher	Springer Verlag
Pages	440-447
Number of pages	8
ISBN (Print)	9783319648606
DOIs	https://doi.org/10.1007/978-3-319-64861-3_41
Publication status	Published - 2018
Event	3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017 - Cairo, Egypt Duration: 9 Sept 2017 → 11 Sept 2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	639
ISSN (Print)	2194-5357

Conference

Conference	3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017
Country/Territory	Egypt
City	Cairo
Period	9/09/17 → 11/09/17

Keywords

Crowdsourcing
Dialectal arabic
Low-resource languages

Access to Document

10.1007/978-3-319-64861-3_41

Cite this

Mubarak, H. (2018). Crowdsourcing speech and language data for resource-poor languages. In M. F. Tolba, T. Gaber, K. Shaalan, & A. E. Hassanien (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017 (pp. 440-447). (Advances in Intelligent Systems and Computing; Vol. 639). Springer Verlag. https://doi.org/10.1007/978-3-319-64861-3_41

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title = "Crowdsourcing speech and language data for resource-poor languages",

abstract = "In this paper, we present benefits of using crowdsourcing to build speech and language resources for different annotation tasks for dialectal Arabic as an example of resource-poor languages. We show recommendations for job design and quality control that allow us to build high quality data for variety of tasks. Most of these recommendations are language-independent and can be applied to other languages as well. We summarize lessons learned from experiments in data acquisition tasks, such as image annotation (transcription of Arabic historical documents), machine translation (translation from English to Hindi), speech annotation (transcription of dialectal Arabic audio files), text annotation (conversion from dialectal Arabic to Modern Standard Arabic (MSA)), and text classification (annotation of offensive language on Arabic social media, and classification of questions on Arabic medical web forums).",

keywords = "Crowdsourcing, Dialectal arabic, Low-resource languages",

author = "Hamdy Mubarak",

note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG.; 3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017 ; Conference date: 09-09-2017 Through 11-09-2017",

year = "2018",

doi = "10.1007/978-3-319-64861-3\_41",

language = "English",

isbn = "9783319648606",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "440--447",

editor = "Tolba, \{Mohamed F.\} and Tarek Gaber and Khaled Shaalan and Hassanien, \{Aboul Ella\}",

booktitle = "Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017",

address = "Germany",

}

Mubarak, H 2018, Crowdsourcing speech and language data for resource-poor languages. in MF Tolba, T Gaber, K Shaalan & AE Hassanien (eds), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. Advances in Intelligent Systems and Computing, vol. 639, Springer Verlag, pp. 440-447, 3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017, Cairo, Egypt, 9/09/17. https://doi.org/10.1007/978-3-319-64861-3_41

Crowdsourcing speech and language data for resource-poor languages. / Mubarak, Hamdy.
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. ed. / Mohamed F. Tolba; Tarek Gaber; Khaled Shaalan; Aboul Ella Hassanien. Springer Verlag, 2018. p. 440-447 (Advances in Intelligent Systems and Computing; Vol. 639).

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

TY - GEN

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AU - Mubarak, Hamdy

PY - 2018

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N2 - In this paper, we present benefits of using crowdsourcing to build speech and language resources for different annotation tasks for dialectal Arabic as an example of resource-poor languages. We show recommendations for job design and quality control that allow us to build high quality data for variety of tasks. Most of these recommendations are language-independent and can be applied to other languages as well. We summarize lessons learned from experiments in data acquisition tasks, such as image annotation (transcription of Arabic historical documents), machine translation (translation from English to Hindi), speech annotation (transcription of dialectal Arabic audio files), text annotation (conversion from dialectal Arabic to Modern Standard Arabic (MSA)), and text classification (annotation of offensive language on Arabic social media, and classification of questions on Arabic medical web forums).

AB - In this paper, we present benefits of using crowdsourcing to build speech and language resources for different annotation tasks for dialectal Arabic as an example of resource-poor languages. We show recommendations for job design and quality control that allow us to build high quality data for variety of tasks. Most of these recommendations are language-independent and can be applied to other languages as well. We summarize lessons learned from experiments in data acquisition tasks, such as image annotation (transcription of Arabic historical documents), machine translation (translation from English to Hindi), speech annotation (transcription of dialectal Arabic audio files), text annotation (conversion from dialectal Arabic to Modern Standard Arabic (MSA)), and text classification (annotation of offensive language on Arabic social media, and classification of questions on Arabic medical web forums).

KW - Crowdsourcing

KW - Dialectal arabic

KW - Low-resource languages

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DO - 10.1007/978-3-319-64861-3_41

M3 - Conference contribution

AN - SCOPUS:85029469138

SN - 9783319648606

T3 - Advances in Intelligent Systems and Computing

SP - 440

EP - 447

BT - Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017

A2 - Tolba, Mohamed F.

A2 - Gaber, Tarek

A2 - Shaalan, Khaled

A2 - Hassanien, Aboul Ella

PB - Springer Verlag

T2 - 3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017

Y2 - 9 September 2017 through 11 September 2017

ER -

Crowdsourcing speech and language data for resource-poor languages

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Conference

Keywords

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