  Event forecasting is a challenging, yet important task, as humans seek to
constantly plan for the future. Existing automated forecasting studies rely
mostly on structured data, such as time-series or event-based knowledge graphs,
to help predict future events. In this work, we aim to formulate a task,
construct a dataset, and provide benchmarks for developing methods for event
forecasting with large volumes of unstructured text data. To simulate the
forecasting scenario on temporal news documents, we formulate the problem as a
restricted-domain, multiple-choice, question-answering (QA) task. Unlike
existing QA tasks, our task limits accessible information, and thus a model has
to make a forecasting judgement. To showcase the usefulness of this task
formulation, we introduce ForecastQA, a question-answering dataset consisting
of 10,392 event forecasting questions, which have been collected and verified
via crowdsourcing efforts. We present our experiments on ForecastQA using
BERT-based models and find that our best model achieves 60.1% accuracy on the
dataset, which still lags behind human performance by about 19%. We hope
ForecastQA will support future research efforts in bridging this gap.
