An Introduction to Crowdsourcing for Language and Multimedia Technology Research

Abstract

Abstract. Language and multimedia technology research often relies on large manually constructed datasets for training or evaluation of algorithms and systems. Constructing these datasets is often expensive with significant challenges in terms of recruitment of personnel to carry out the work. Crowdsourcing methods using scalable pools of workers available on-demand offers a flexible means of rapid low-cost construction of many of these datasets to support existing research requirements and potentially promote new research initiatives that would otherwise not be possible.

Research on the Applications of Multimedia Technology under the Current Epoch of Internet Plus

“…Data compression technology refers to the graphics, audio and video signal compression, storage and the distribution, we through improving the efficiency of the communication mains transmission can provide more effective means for the technology application, at the same time, computer control center can be real-time monitoring and processing of audio and video information, which can guarantee the output of high quality sound, practical video program. As known to all, multimedia computer technology popularization gradually, it can release a large number of multimedia information resources on the web, whether or not some information resources can be effective organization management and retrieval will rely on the index of the image and media content [5][6].…”

In Search of Ambiguity: A Three-Stage Workflow Design to Clarify Annotation Guidelines for Crowd Workers

“…A variety of tutorials, surveys, introductions, and research articles offer how-to advice for successful microtask crowdsourcing with platforms such as MTurk (Jones, 2013 ; Marshall and Shipman, 2013 ; Egelman et al, 2014 ; Kovashka et al, 2016 ). For example, it is often recommended that requesters invest time browsing and labeling some data themselves before launching a task in order to better define and debug it (Alonso, 2015 ).…”

In Search of Ambiguity: A Three-Stage Workflow Design to Clarify Annotation Guidelines for Crowd Workers

“…Best practices. A variety of tutorials, surveys, introductions, and research papers offer how-to advice for successful microtask crowdsourcing with platforms such as MTurk (Kovashka et al, 2016;Egelman et al, 2014;Jones, 2013;Marshall and Shipman, 2013). For example, it is often recommended that requesters invest time browsing and labeling some data themselves before launching a task in order to better define and debug it (Alonso, 2015).…”