Abstract

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. We evaluate CrowdLang by developing a text translation program incorporating human and machine agents. The evaluation shows that we are able to simply explore a large design space of possible problem solving programs with the simple variation of the used abstractions. Furthermore, an experiment, involving 1918 different human actors, shows that the developed mixed human-machine translation program significantly outperforms a pure machine translation in terms of adequacy and fluency whilst translating more than 30 pages per hour and that the program approximates the professional translated gold-standard to 75% using the automatic evaluation metric METEOR. Last but not least, our evaluation illustrates that our new human computation pattern staged-contest with pruning outperforms all other refinements in the translation task.