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|Title||CrowdLang: programming human computation systems|
|Abstract Text||Today, human computation systems are mostly used for batch processing large amount of data in a variety of tasks (e.g., image labeling or optical character recognition) and, often, the applications are the result of extensive lengthy trial-and-error refinements. A plethora of tasks, however, cannot be captured in this paradigm and as we move to more sophisticated problem solving, we will need to rethink the way in which we coordinate networked humans and computers involved in a task. What we lack is an approach to engineer solutions based on past successful patterns. In this paper we present the programming language and framework CrowdLang for engineering complex computation systems incorporating large numbers of networked humans and machines agents incorporating a library of known successful interaction patterns. CrowdLang allows to design complex problem solving tasks that combine large numbers of human and machine actors whilst incorporating known successful patterns. We evaluate CrowdLang by programming a text translation task using a variety of different known human-computation patterns. The evaluation shows that CrowdLang is able to simply explore a large design space of possible problem solving programs with the simple variation of the used abstractions. In an experiment involving 1918 different human actors we, furthermore, show that a mixed human-machine translation significantly outperforms a pure machine translation in terms of adequacy and fluency whilst translating more than 30 pages per hour and that the mixed translation approximates the human-translated gold-standard to 75% using the automatic evaluation metric METEOR. Last but not least, our evaluation illustrates that a new human-computation pattern, which we call staged-contest with pruning, outperforms all other refinements in the translation task.|