AI-based Request Augmentation to Increase Crowdsourcing Participation

Junwon Park, Ranjay Krishna, Pranav Khadpe, Li Fei-Fei, Michael Bernstein

To support the massive data requirements of modern supervised machine learning (ML) algorithms, crowdsourcing systems match volunteer contributors to appropriate tasks. Such systems learn “what” types of tasks contributors are interested to complete. In this paper, instead of focusing on “what” to ask, we focus on learning “how” to ask: how to make relevant and interesting requests to encourage crowdsourcing participation. We introduce a new technique that augments questions with ML-based request strategies drawn from social psychology. We also introduce a contextual bandit algorithm to select which strategy to apply for a given task and contributor. We deploy our approach to collect volunteer data from Instagram for the task of visual question answering (VQA), an important task in computer vision and natural language processing that has enabled numerous human-computer interaction applications. For example, when encountering a user’s Instagram post that contains the ornate Trevi Fountain in Rome, our approach learns to augment its original raw question “Where is this place?” with image-relevant compliments such as “What a great statue!” or with travel-relevant justifications such as “I would like to visit this place”, increasing the user’s likelihood of answering the question and thus providing a label. We deploy our agent on Instagram to ask questions about social media images, finding that the response rate improves from 15.8% with unaugmented questions to 30.54% with baseline rule-based strategies and to 58.1% with ML-based strategies.

The research was published in AAAI Conference on Human Computation and Crowdsourcing on 10/20/2019. The research is supported by the Brown Institute Magic Grant for the project Learning to Engage in Conversations with AI Systems.

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