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Event forecasting is a challenging, yet important task, as humans seek toconstantly plan for the future. Existing automated forecasting studies relymostly 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 eventforecasting with large volumes of unstructured text data. To simulate theforecasting scenario on temporal news documents, we formulate the problem as arestricted-domain, multiple-choice, question-answering (QA) task. Unlikeexisting QA tasks, our task limits accessible information, and thus a model hasto make a forecasting judgement. To showcase the usefulness of this taskformulation, we introduce ForecastQA, a question-answering dataset consistingof 10,392 event forecasting questions, which have been collected and verifiedvia crowdsourcing efforts. We present our experiments on ForecastQA usingBERT-based models and find that our best model achieves 60.1% accuracy on thedataset, which still lags behind human performance by about 19%. We hopeForecastQA will support future research efforts in bridging this gap.

ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data