2021
EMNLP
EMNLP 2021
Written Justifications are Key to Aggregate Crowdsourced Forecasts
Abstract
AbstractThis paper demonstrates that aggregating crowdsourced forecasts benefits from modeling the written justifications provided by forecasters. Our experiments show that the majority and weighted vote baselines are competitive, and that the written justifications are beneficial to call a question throughout its life except in the last quarter. We also conduct an error analysis shedding light into the characteristics that make a justification unreliable.
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Interdisciplinary Bridge
— Data Science & Analytics and Interdisciplinary and Machine Learning
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Keyword Pioneer
— forecast aggregation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio