Abstract
Time is an important dimension in our physical world. Lots of facts can evolve with respect to time. For example, the U.S. President might change every four years. Therefore, it is important to consider the time dimension and empower the existing QA models to reason over time. However, the existing QA datasets contain rather few time-sensitive questions, hence not suitable for diagnosing or benchmarking the model's temporal reasoning capability. In order to promote research in this direction, we propose to construct a time-sensitive QA dataset. The dataset is constructed by 1) mining time-evolving facts from WikiData and align them to their corresponding Wikipedia page, 2) employing crowd workers to verify and calibrate these noisy facts, 3) generating question-answer pairs based on the annotated time-sensitive facts. Our dataset poses two novel challenges: 1) the model needs to understand both explicit and implicit mention of time information in the long document, 2) the model needs to perform temporal reasoning like comparison, addition, subtraction. We evaluate different SoTA long-document QA systems like BigBird and FiD on our dataset. The best-performing model FiD can only achieve 46\% accuracy, still far behind the human performance of 87\%. We demonstrate that these models are still lacking the ability to perform robust temporal understanding and reasoning. Therefore, we believe that our dataset could serve as a benchmark to empower future studies in temporal reasoning. The dataset and code are released in~\url{this https URL}.
Abstract (translated)
URL
https://arxiv.org/abs/2108.06314
