Improving Event Duration Prediction via Time-aware Pre-training

• URL: http://arxiv.org/abs/2011.02610v1
• Date: Thu, 5 Nov 2020 01:52:11 GMT
• Title: Improving Event Duration Prediction via Time-aware Pre-training
• Authors: Zonglin Yang, Xinya Du, Alexander Rush, Claire Cardie
• Abstract summary: We introduce two effective models for duration prediction. 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.
• Score: 90.74988936678723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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