ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data
Event forecasting is a challenging, yet consequential task, as humans seek to constantly plan for the future. Existing automated forecasting approaches rely mostly on structured data, such as time series or event based knowledge graphs, to help predict future events. In this work, we formulate the forecasting problem as a restricted domain, multiple choice, question answering (QA) task that simulates the forecasting scenario. To showcase the usefulness of this task formulation, we introduce a dataset ForecastQA, a question answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts. We also present our experiments on ForecastQA using BERT based models and find that our best model achieves 61.0\ accuracy on the dataset, which is still far behind human performance by about 18% We hope ForecastQA will support future research efforts in bridging this gap.
ACL/IJCNLP