EMNLP 2021Finding
Written Justifications are Key to Aggregate Crowdsourced Forecasts
作者: Saketh Kotamraju, Eduardo Blanco
摘要
This 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.