@inproceedings{kicikoglu-etal-2020-aggregation,
title = "Aggregation Driven Progression System for {GWAP}s",
author = "Kicikoglu, Osman Doruk and
Bartle, Richard and
Chamberlain, Jon and
Paun, Silviu and
Poesio, Massimo",
editor = "Lukin, Stephanie M.",
booktitle = "Workshop on Games and Natural Language Processing",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.gamnlp-1.11",
pages = "79--84",
abstract = "As the uses of Games-With-A-Purpose (GWAPs) broadens, the systems that incorporate its usages have expanded in complexity. The types of annotations required within the NLP paradigm set such an example, where tasks can involve varying complexity of annotations. Assigning more complex tasks to more skilled players through a progression mechanism can achieve higher accuracy in the collected data while acting as a motivating factor that rewards the more skilled players. In this paper, we present the progression technique implemented in Wormingo , an NLP GWAP that currently includes two layers of task complexity. For the experiment, we have implemented four different progression scenarios on 192 players and compared the accuracy and engagement achieved with each scenario.",
language = "English",
ISBN = "979-10-95546-40-5",
}
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<abstract>As the uses of Games-With-A-Purpose (GWAPs) broadens, the systems that incorporate its usages have expanded in complexity. The types of annotations required within the NLP paradigm set such an example, where tasks can involve varying complexity of annotations. Assigning more complex tasks to more skilled players through a progression mechanism can achieve higher accuracy in the collected data while acting as a motivating factor that rewards the more skilled players. In this paper, we present the progression technique implemented in Wormingo , an NLP GWAP that currently includes two layers of task complexity. For the experiment, we have implemented four different progression scenarios on 192 players and compared the accuracy and engagement achieved with each scenario.</abstract>
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%0 Conference Proceedings
%T Aggregation Driven Progression System for GWAPs
%A Kicikoglu, Osman Doruk
%A Bartle, Richard
%A Chamberlain, Jon
%A Paun, Silviu
%A Poesio, Massimo
%Y Lukin, Stephanie M.
%S Workshop on Games and Natural Language Processing
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-40-5
%G English
%F kicikoglu-etal-2020-aggregation
%X As the uses of Games-With-A-Purpose (GWAPs) broadens, the systems that incorporate its usages have expanded in complexity. The types of annotations required within the NLP paradigm set such an example, where tasks can involve varying complexity of annotations. Assigning more complex tasks to more skilled players through a progression mechanism can achieve higher accuracy in the collected data while acting as a motivating factor that rewards the more skilled players. In this paper, we present the progression technique implemented in Wormingo , an NLP GWAP that currently includes two layers of task complexity. For the experiment, we have implemented four different progression scenarios on 192 players and compared the accuracy and engagement achieved with each scenario.
%U https://aclanthology.org/2020.gamnlp-1.11
%P 79-84
Markdown (Informal)
[Aggregation Driven Progression System for GWAPs](https://aclanthology.org/2020.gamnlp-1.11) (Kicikoglu et al., GAMESandNLP 2020)
ACL
- Osman Doruk Kicikoglu, Richard Bartle, Jon Chamberlain, Silviu Paun, and Massimo Poesio. 2020. Aggregation Driven Progression System for GWAPs. In Workshop on Games and Natural Language Processing, pages 79–84, Marseille, France. European Language Resources Association.