Crowdsourcing a text corpus for a low resource language

 

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dc.contributor.advisor Suleman, Hussein en_ZA
dc.contributor.author Packham, Sean en_ZA
dc.date.accessioned 2016-07-18T12:55:04Z
dc.date.available 2016-07-18T12:55:04Z
dc.date.issued 2016 en_ZA
dc.identifier.citation Packham, S. 2016. Crowdsourcing a text corpus for a low resource language. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/20436
dc.description.abstract Low resourced languages, such as South Africa's isiXhosa, have a limited number of digitised texts, making it challenging to build language corpora and the information retrieval services, such as search and translation that depend on them. Researchers have been unable to assemble isiXhosa corpora of sufficient size and quality to produce working machine translation systems and it has been acknowledged that there is little to know training data and sourcing translations from professionals can be a costly process. A crowdsourcing translation game which paid participants for their contributions was proposed as a solution to source original and relevant parallel corpora for low resource languages such as isiXhosa. The objectives of this dissertation is to report on the four experiments that were conducted to assess user motivation and contribution quantity under various scenarios using the developed crowdsourcing translation game. The first experiment was a pilot study to test a custom built system and to find out if social network users would volunteer to participate in a translation game for free. The second experiment tested multiple payment schemes with users from the University of Cape Town. The schemes rewarded users with consistent, increasing or decreasing amounts for subsequent contributions. Experiment 3 tested whether the same users from Experiment 2 would continue contributing if payments were taken away. The last experiment tested a payment scheme that did not offer a direct and guaranteed reward. Users were paid based on their leaderboard placement and only a limited number of the top leaderboard spots were allocated rewards. From experiment 1 and 3 we found that people do not volunteer without financial incentives, experiment 2 and 4 showed that people want increased rewards when putting in increased effort , experiment 3 also showed that people will not continue contributing if the financial incentives are taken away and experiment 4 also showed that the possibility of incentives is as attractive as offering guaranteed incentives . en_ZA
dc.language.iso eng en_ZA
dc.subject.other Computer Science en_ZA
dc.title Crowdsourcing a text corpus for a low resource language en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Department of Computer Science en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Packham, S. (2016). <i>Crowdsourcing a text corpus for a low resource language</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/20436 en_ZA
dc.identifier.chicagocitation Packham, Sean. <i>"Crowdsourcing a text corpus for a low resource language."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2016. http://hdl.handle.net/11427/20436 en_ZA
dc.identifier.vancouvercitation Packham S. Crowdsourcing a text corpus for a low resource language. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2016 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/20436 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Packham, Sean AB - Low resourced languages, such as South Africa's isiXhosa, have a limited number of digitised texts, making it challenging to build language corpora and the information retrieval services, such as search and translation that depend on them. Researchers have been unable to assemble isiXhosa corpora of sufficient size and quality to produce working machine translation systems and it has been acknowledged that there is little to know training data and sourcing translations from professionals can be a costly process. A crowdsourcing translation game which paid participants for their contributions was proposed as a solution to source original and relevant parallel corpora for low resource languages such as isiXhosa. The objectives of this dissertation is to report on the four experiments that were conducted to assess user motivation and contribution quantity under various scenarios using the developed crowdsourcing translation game. The first experiment was a pilot study to test a custom built system and to find out if social network users would volunteer to participate in a translation game for free. The second experiment tested multiple payment schemes with users from the University of Cape Town. The schemes rewarded users with consistent, increasing or decreasing amounts for subsequent contributions. Experiment 3 tested whether the same users from Experiment 2 would continue contributing if payments were taken away. The last experiment tested a payment scheme that did not offer a direct and guaranteed reward. Users were paid based on their leaderboard placement and only a limited number of the top leaderboard spots were allocated rewards. From experiment 1 and 3 we found that people do not volunteer without financial incentives, experiment 2 and 4 showed that people want increased rewards when putting in increased effort , experiment 3 also showed that people will not continue contributing if the financial incentives are taken away and experiment 4 also showed that the possibility of incentives is as attractive as offering guaranteed incentives . DA - 2016 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2016 T1 - Crowdsourcing a text corpus for a low resource language TI - Crowdsourcing a text corpus for a low resource language UR - http://hdl.handle.net/11427/20436 ER - en_ZA


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