论文标题
促进混合人群预测聚合的关注
Anchor Attention for Hybrid Crowd Forecasts Aggregation
论文作者
论文摘要
在人群预测系统中，聚合是一种算法，它根据人群中每个人提供的每个问题提供了每个问题的汇总概率。已经提出了各种聚合方法，但是线性平均或选择表现最佳的个体（例如线性平均）的简单策略仍然具有竞争力。随着神经网络的最新进展，我们将聚合作为机器翻译任务进行建模，该任务从单个预测的顺序转化为汇总的预测，基于问题和预测者之间的拟议锚点的注意。我们使用在预测平台上收集的数据和公开良好的判断项目数据集评估了我们的方法，并证明我们的方法通过学习预报器和问题的良好表示，优于当前最新的聚合方法。
In a crowd forecasting system, aggregation is an algorithm that returns aggregated probabilities for each question based on the probabilities provided per question by each individual in the crowd. Various aggregation methods have been proposed, but simple strategies like linear averaging or selecting the best-performing individual remain competitive. With the recent advance in neural networks, we model forecasts aggregation as a machine translation task, that translates from a sequence of individual forecasts into aggregated forecasts, based on proposed Anchor Attention between questions and forecasters. We evaluate our approach using data collected on our forecasting platform and publicly available Good Judgement Project dataset, and show that our method outperforms current state-of-the-art aggregation approaches by learning a good representation of forecaster and question.
