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Register a new userMillionaire: A Hint-guided Approach for Crowdsourcing
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Bo Han
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Modern machine learning is migrating to the era of complex models, which requires a plethora of well-annotated data. While crowdsourcing is a promising tool to achieve this goal, existing crowdsourcing approaches barely acquire a sufficient amount of high-quality labels. In this paper, motivated by the Guess-with-Hints answer strategy from the Millionaire game show, we introduce the hint-guided approach into crowdsourcing to deal with this challenge. Our approach encourages workers to get help from hints when they are unsure of questions. Specifically, we propose a hybrid-stage setting, consisting of the main stage and the hint stage. When workers face any uncertain question on the main stage, they are allowed to enter the hint stage and look up hints before making any answer. A unique payment mechanism that meets two important design principles for crowdsourcing is developed. Besides, the proposed mechanism further encourages high-quality workers less using hints, which helps identify and assigns larger possible payment to them. Experiments are performed on Amazon Mechanical Turk, which show that our approach ensures a sufficient number of high-quality labels with low expenditure and detects high-quality workers.
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We investigated the feasibility of crowdsourcing full-fledged tutorial videos from ordinary people on the Web on how to solve math problems related to logarithms. This kind of approach (a form of learnersourcing) to efficiently collecting tutorial videos and other learning resources could be useful for realizing personalized learning-at-scale, whereby students receive specific learning resources -- drawn from a large and diverse set -- that are tailored to their individual and time-varying needs. Results of our study, in which we collected 399 videos from 66 unique teachers on Mechanical Turk, suggest that (1) approximately 100 videos -- over $80%$ of which are mathematically fully correct -- can be crowdsourced per week for $5/video; (2) the crowdsourced videos exhibit significant diversity in terms of language style, presentation media, and pedagogical approach; (3) the average learning gains (posttest minus pretest score) associated with watching the videos was stat.~sig.~higher than for a control video ($0.105$ versus $0.045$); and (4) the average learning gains ($0.1416$) from watching the best tested crowdsourced videos was comparable to the learning gains ($0.1506$) from watching a popular Khan Academy video on logarithms.
In this paper we propose applying the crowdsourcing approach to a software platform that uses a modern and state-of-the-art 3D game engine. This platform could facilitate the generation and manipulation of interactive 3D environments by a community of users producing different content such as cultural heritage, scientific virtual labs, games, novel art forms and virtual museums.
Eye movements provide insight into what parts of an image a viewer finds most salient, interesting, or relevant to the task at hand. Unfortunately, eye tracking data, a commonly-used proxy for attention, is cumbersome to collect. Here we explore an alternative: a comprehensive web-based toolbox for crowdsourcing visual attention. We draw from four main classes of attention-capturing methodologies in the literature. ZoomMaps is a novel zoom-based interface that captures viewing on a mobile phone. CodeCharts is a self-reporting methodology that records points of interest at precise viewing durations. ImportAnnots is an annotation tool for selecting important image regions, and cursor-based BubbleView lets viewers click to deblur a small area. We compare these methodologies using a common analysis framework in order to develop appropriate use cases for each interface. This toolbox and our analyses provide a blueprint for how to gather attention data at scale without an eye tracker.
Large-scale labeled datasets are the indispensable fuel that ignites the AI revolution as we see today. Most such datasets are constructed using crowdsourcing services such as Amazon Mechanical Turk which provides noisy labels from non-experts at a fair price. The sheer size of such datasets mandates that it is only feasible to collect a few labels per data point. We formulate the problem of test-time label aggregation as a statistical estimation problem of inferring the expected voting score in an ideal world where all workers label all items. By imitating workers with supervised learners and using them in a doubly robust estimation framework, we prove that the variance of estimation can be substantially reduced, even if the learner is a poor approximation. Synthetic and real-world experiments show that by combining the doubly robust approach with adaptive worker/item selection, we often need as low as 0.1 labels per data point to achieve nearly the same accuracy as in the ideal world where all workers label all data points.
The main goal of this paper is to discuss how to integrate the possibilities of crowdsourcing platforms with systems supporting workflow to enable the engagement and interaction with business tasks of a wider group of people. Thus, this work is an attempt to expand the functional capabilities of typical business systems by allowing selected process tasks to be performed by unlimited human resources. Opening business tasks to crowdsourcing, within established Business Process Management Systems (BPMS) will improve the flexibility of company processes and allow for lower work-load and greater specialization among the staff employed on-site. The presented conceptual work is based on the current international standards in this field, promoted by Workflows Management Coalition. To this end, the functioning of business platforms was analysed and their functionality was presented visually, followed by a proposal and a discussion of how to implement crowdsourcing into workflow systems.
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