锚点注意力在混合人群预测聚集中的应用
Anchor Attention for Hybrid Crowd Forecasts Aggregation
摘要：
在人群预测系统中，聚合是一种算法，该算法基于人群中每个个体提供的每个问题的概率来返回每个问题的聚合概率。已经提出了各种聚合方法，但简单的策略，如线性平均或选择表现最好的个体仍然具有竞争力。随着神经网络的最新进展，我们将预测聚合建模为机器翻译任务，该任务基于问题和预测者之间的锚点注意力，将单个预测序列转换为聚合预测。我们使用在我们的预测平台上收集的数据和公开的Good Judgement Project数据集来评估我们的方法，并通过学习预测者和问题的良好表示来表明我们的方法优于当前最先进的聚合方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
