  End-to-end models in NLP rarely encode external world knowledge about length
of time. We introduce two effective models for duration prediction, which
incorporate external knowledge by reading temporal-related news sentences
(time-aware pre-training). Specifically, one model predicts the range/unit
where the duration value falls in (R-pred); and the other predicts the exact
duration value E-pred. Our best model -- E-pred, substantially outperforms
previous work, and captures duration information more accurately than R-pred.
We also demonstrate our models are capable of duration prediction in the
unsupervised setting, outperforming the baselines.
