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        <title>Improving Event Duration Prediction via Time-aware Pre-training</title>
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        <namePart type="given">Zonglin</namePart>
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        <namePart type="given">Xinya</namePart>
        <namePart type="family">Du</namePart>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
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    <name type="personal">
        <namePart type="given">Claire</namePart>
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            <namePart type="given">Yulan</namePart>
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            <namePart type="given">Yang</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.302</identifier>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Improving Event Duration Prediction via Time-aware Pre-training
%A Yang, Zonglin
%A Du, Xinya
%A Rush, Alexander
%A Cardie, Claire
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F yang-etal-2020-improving
%X 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.
%R 10.18653/v1/2020.findings-emnlp.302
%U https://aclanthology.org/2020.findings-emnlp.302
%U https://doi.org/10.18653/v1/2020.findings-emnlp.302
%P 3370-3378

Markdown (Informal)

[Improving Event Duration Prediction via Time-aware Pre-training](https://aclanthology.org/2020.findings-emnlp.302) (Yang et al., Findings 2020)

• Improving Event Duration Prediction via Time-aware Pre-training (Yang et al., Findings 2020)

ACL

• Zonglin Yang, Xinya Du, Alexander Rush, and Claire Cardie. 2020. Improving Event Duration Prediction via Time-aware Pre-training. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3370–3378, Online. Association for Computational Linguistics.