Abstract | ||
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Many result inference methods have been proposed to address the quality-control problem in crowdsourcing. However, existing methods are ineffective for context-sensitive tasks (CSTs), e.g., handwriting recognition, translation, speech transcription, where context correlation within a task cannot be ignored for two reasons. Firstly, it is ineffective to crowdsource a whole CST (e.g., recognizing handwritten texts) and use task-level inference methods to infer the answer, because it is rather hard to correctly complete a whole complicated task. Secondly, although a CST is composed of a set of atomic subtasks (e.g., recognizing a handwritten word), it is unsuitable to split it into multiple subtasks and adopt a subtask-level inference algorithm to infer the result, because this will lose the context correlation (e.g., phrases) among subtasks and increase the difficulty to complete a task. Thus it calls for a new approach to handling CSTs. In this work, we study the result inference problem for CSTs and propose a context-aware inference algorithm. We design an inference algorithm by incorporating the context information. Furthermore, we introduce an iterative method to improve the quality. The results of experiments on real-world CSTs demonstrated the superiority of our approach compared with the state-of-the-art methods. |
Year | DOI | Venue |
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2018 | 10.1016/j.ins.2018.05.050 | Information Sciences |
Keywords | Field | DocType |
Crowdsourcing,Human computation,Quality control,Context-sensitive tasks | Speech transcription,Inference,Crowdsourcing,Iterative method,Handwriting recognition,Crowdsource,Correlation,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
460 | 0020-0255 | 2 |
PageRank | References | Authors |
0.35 | 34 | 5 |
Name | Order | Citations | PageRank |
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Yili Fang | 1 | 39 | 4.41 |
Hailong Sun | 2 | 680 | 64.83 |
Guoliang Li | 3 | 3077 | 154.70 |
Richong Zhang | 4 | 232 | 39.67 |
Jinpeng Huai | 5 | 1187 | 130.18 |