Crowdsourced Accessibility: Elicitation of Wikipedia Articles

Mechanical Turk is useful for generating complex speech resources like conversational speech transcription. In this work, we explore the next step of eliciting narrations of Wikipedia articles to improve accessibility for low-literacy users. This task proves a useful test-bed to implement qualitative vetting of workers based on difficult to define metrics like narrative quality. Working with the Mechanical Turk API, we collected sample narrations, had other Turkers rate these samples and then granted access to full narration HITs depending on aggregate quality. While narrating full articles proved too onerous a task to be viable, using other Turkers to perform vetting was very successful. Elicitation is possible on Mechanical Turk, but it should conform to suggested best practices of simple tasks that can be completed in a streamlined workflow.