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        <title>Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event</title>
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        <namePart type="given">Nazanin</namePart>
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            <title>Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
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    <abstract>We propose an effective context-sensitive neural model for time to event (TTE) prediction task, which aims to predict the amount of time to/from the occurrence of given events in streaming content. We investigate this problem in the context of a multi-task learning framework, which we enrich with time difference embeddings. In addition, we develop a multi-genre dataset of English events about soccer competitions and academy awards ceremonies, and their relevant tweets obtained from Twitter. Our model is 1.4 and 3.3 hours more accurate than the current state-of-the-art model in estimating TTE on English and Dutch tweets respectively. We examine different aspects of our model to illustrate its source of improvement.</abstract>
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%0 Conference Proceedings
%T Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event
%A Dehghani, Nazanin
%A Hajipoor, Hassan
%A Amiri, Hadi
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F dehghani-etal-2021-embedding
%X We propose an effective context-sensitive neural model for time to event (TTE) prediction task, which aims to predict the amount of time to/from the occurrence of given events in streaming content. We investigate this problem in the context of a multi-task learning framework, which we enrich with time difference embeddings. In addition, we develop a multi-genre dataset of English events about soccer competitions and academy awards ceremonies, and their relevant tweets obtained from Twitter. Our model is 1.4 and 3.3 hours more accurate than the current state-of-the-art model in estimating TTE on English and Dutch tweets respectively. We examine different aspects of our model to illustrate its source of improvement.
%R 10.18653/v1/2021.acl-short.80
%U https://aclanthology.org/2021.acl-short.80
%U https://doi.org/10.18653/v1/2021.acl-short.80
%P 630-636

Markdown (Informal)

[Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event](https://aclanthology.org/2021.acl-short.80) (Dehghani et al., ACL-IJCNLP 2021)

• Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event (Dehghani et al., ACL-IJCNLP 2021)

ACL

• Nazanin Dehghani, Hassan Hajipoor, and Hadi Amiri. 2021. Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 630–636, Online. Association for Computational Linguistics.