Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We here show that the resulting budget can be used more effectively with a flexible worker assignment strategy that asks fewer workers to analyze easy-to-label data and more workers to analyze data that requires extra scrutiny. Our main contribution is to show how the allocations of the number of workers to a task can be computed optimally based on task features alone, without using worker profiles. Our target tasks are delineating cells in microscopy images and analyzing the sentiment toward the 2016 U.S. presidential candidates in tweets. We first propose an algorithm that computes budget-optimized crowd worker allocation (BUOCA). We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling. We show that the computed allocation can yield large savings in the crowdsourcing budget (up to 49 percent points) while maintaining labeling accuracy. Finally, we envisage a human-machine system for performing budget-optimized data analysis at a scale beyond the feasibility of crowdsourcing.
Related Crowdsourcing Methodologies. Balancing the demands that accuracy requirements and budget limits place on crowdsourcing experiments has been the focus of research in various communities, including machine learning , human computation , data management , and computer vision . The crowdsourcing mechanisms used in practice, e.g., collecting image labels to train computer vision systems, are typically agnostic to the difficulty of a task, assigning the same fixed number of crowd workers to each task. Notable exceptions are the recent works by @cite_10 , @cite_0 , and @cite_2 , who proposed flexible worker assignment schemes.
@cite_0 @cite_10 @cite_2
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An increasing number of studies in political communication focus on the “sentiment” or “tone” of news content, political speeches, or advertisements. This growing interest in measuring sentiment coincides with a dramatic increase in the volume of digitized information. Computer automation has a great deal of potential in this new media environment. The objective here is to outline and validate a new automated measurement instrument for sentiment analysis in political texts. Our instrument uses a dictionary-based approach consisting of a simple word count of the frequency of keywords in a text from a predefined dictionary. The design of the freely available Lexicoder Sentiment Dictionary (LSD) is discussed in detail here. The dictionary is tested against a body of human-coded news content, and the resulting codes are also compared to results from nine existing content-analytic dictionaries. Analyses suggest that the LSD produces results that are more systematically related to human coding than are results ... We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems. Specifically, we distinguish between images which lead multiple annotators to segment different foreground objects (ambiguous) versus minor inter-annotator differences of the same object. Taking images from eight widely used datasets, we crowdsource labeling the images as “ambiguous” or “not ambiguous” to segment in order to construct a new dataset we call STATIC. Using STATIC, we develop a system that automatically predicts which images are ambiguous. Experiments demonstrate the advantage of our prediction system over existing saliency-based methods on images from vision benchmarks and images taken by blind people who are trying to recognize objects in their environment. Finally, we introduce a crowdsourcing system to achieve cost savings for collecting the diversity of all valid “ground truth” foreground object segmentations by collecting extra segmentations only when ambiguity is expected. Experiments show our system eliminates up to 47 of human effort compared to existing crowdsourcing methods with no loss in capturing the diversity of ground truths. Visual question answering systems empower users to ask any question about any image and receive a valid answer. However, existing systems do not yet account for the fact that a visual question can lead to a single answer or multiple different answers. While a crowd often agrees, disagreements do arise for many reasons including that visual questions are ambiguous, subjective, or difficult. We propose a model, CrowdVerge, for automatically predicting from a visual question whether a crowd would agree on one answer. We then propose how to exploit these predictions in a novel application to efficiently collect all valid answers to visual questions. Specifically, we solicit fewer human responses when answer agreement is expected and more human responses otherwise. Experiments on 121,811 visual questions asked by sighted and blind people show that, compared to existing crowdsourcing systems, our system captures the same answer diversity with typically 14-23 less crowd involvement.