  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.
