ForecastQA: Machine Comprehension of Temporal Text for Answering Forecasting Questions
Year: 2020
Type: preprint
Abstract: Textual data are often accompanied by time information (e.g., dates in news articles), but the information is easily overlooked on existing question answering datasets. In this paper, we introduce ForecastQA, a new open-domain question answering dataset consisting of 10k questions which requires temporal reasoning. ForecastQA is collected via a crowdsourcing effort based on news articles, where workers were asked to come up with yes-no or multiple-choice questions. We also present baseline models for our dataset, which is based on a pre-trained language model. In our study, our baseline model achieves 61.6% accuracy on the ForecastQA dataset. We expect that our new data will support future research efforts. Our data and code are publicly available at this https URL.
Source: arXiv (Cornell University)
Authors: Woojeong Jin, Suji Kim, Xiang Ren
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3022752114