BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Toward multi-engine machine translation
HLT '94 Proceedings of the workshop on Human Language Technology
TurKit: tools for iterative tasks on mechanical Turk
Proceedings of the ACM SIGKDD Workshop on Human Computation
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Fast, cheap, and creative: evaluating translation quality using Amazon's Mechanical Turk
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Translation by iterative collaboration between monolingual users
Proceedings of the ACM SIGKDD Workshop on Human Computation
Soylent: a word processor with a crowd inside
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
The efficacy of human post-editing for language translation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Sharing Knowledge and Expertise: The CSCW View of Knowledge Management
Computer Supported Cooperative Work
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In this paper we explore the challenges in crowdsourcing the task of translation over the web in which remotely located translators work on providing translations independent of each other. We then propose a collaborative workflow for crowdsourcing translation to address some of these challenges. In our pipeline model, the translators are working in phases where output from earlier phases can be enhanced in the subsequent phases. We also highlight some of the novel contributions of the pipeline model like assistive translation and translation synthesis that can leverage monolingual and bilingual speakers alike. We evaluate our approach by eliciting translations for both a minority-to-majority language pair and a minority-to-minority language pair. We observe that in both scenarios, our workflow produces better quality translations in a cost-effective manner, when compared to the traditional crowdsourcing workflow.