Improving Worker Engagement Through Conversational Microtask Crowdsourcing
Improving Worker Engagement Through Conversational Microtask Crowdsourcing
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DOI：
10.1145/3313831.3376403
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
S. Qiu;U. Gadiraju;A. Bozzon
中科院分区：
文献类型：
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作者：
S. Qiu;U. Gadiraju;A. Bozzon
对话代理的流行使人类能够更自然地与机器互动。最近的研究表明，微任务市场中的群组工作人员可以使用对话界面完成各种人类智能任务(HITS)，与传统的Web界面相比，对话界面的输出质量相似。在这篇文章中，我们调查了在微任务众包中使用对话界面来提高员工敬业度的有效性。我们设计了一个基于文本的会话代理，帮助工作人员执行任务，并测试了工作人员与不同会话风格的代理交互时的性能。我们对亚马逊土耳其机械师进行了一项严格的实验研究，考察了会话主体及其会话风格是否会影响员工的产出质量、员工敬业度和感知认知负荷。我们的结果表明，对话界面可以有效地吸引员工，而合适的对话风格有可能提高员工的敬业度。
The rise in popularity of conversational agents has enabled humans to interact with machines more naturally. Recent work has shown that crowd workers in microtask marketplaces can complete a variety of human intelligence tasks (HITs) using conversational interfaces with similar output quality compared to the traditional Web interfaces. In this paper, we investigate the effectiveness of using conversational interfaces to improve worker engagement in microtask crowdsourcing. We designed a text-based conversational agent that assists workers in task execution, and tested the performance of workers when interacting with agents having different conversational styles. We conducted a rigorous experimental study on Amazon Mechanical Turk with 800 unique workers, to explore whether the output quality, worker engagement and the perceived cognitive load of workers can be affected by the conversational agent and its conversational styles. Our results show that conversational interfaces can be effective in engaging workers, and a suitable conversational style has potential to improve worker engagement.