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

@inproceedings{Jin2021ForecastQAAQ,
  title={ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data},
  author={Woojeong Jin and Suji Kim and Rahul Khanna and Dong-Ho Lee and Fred Morstatter and A. G. Galstyan and Xiang Ren},
  booktitle={ACL},
  year={2021}
}
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… 
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