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    <titleInfo>
        <title>Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event</title>
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    <name type="personal">
        <namePart type="given">Sarthak</namePart>
        <namePart type="family">Khanal</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Training a robust and reliable deep learning model requires a large amount of data. In the crisis domain, building deep learning models to identify actionable information from the huge influx of data posted by eyewitnesses of crisis events on social media, in a time-critical manner, is central for fast response and relief operations. However, building a large, annotated dataset to train deep learning models is not always feasible in a crisis situation. In this paper, we investigate a multi-task learning approach to concurrently leverage available annotated data for several related tasks from the crisis domain to improve the performance on a main task with limited annotated data. Specifically, we focus on using multi-task learning to improve the performance on the task of identifying location mentions in crisis tweets.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.340</identifier>
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        <date>2021-11</date>
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            <start>4051</start>
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%0 Conference Proceedings
%T Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event
%A Khanal, Sarthak
%A Caragea, Doina
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F khanal-caragea-2021-multi-task
%X Training a robust and reliable deep learning model requires a large amount of data. In the crisis domain, building deep learning models to identify actionable information from the huge influx of data posted by eyewitnesses of crisis events on social media, in a time-critical manner, is central for fast response and relief operations. However, building a large, annotated dataset to train deep learning models is not always feasible in a crisis situation. In this paper, we investigate a multi-task learning approach to concurrently leverage available annotated data for several related tasks from the crisis domain to improve the performance on a main task with limited annotated data. Specifically, we focus on using multi-task learning to improve the performance on the task of identifying location mentions in crisis tweets.
%R 10.18653/v1/2021.findings-emnlp.340
%U https://aclanthology.org/2021.findings-emnlp.340
%U https://doi.org/10.18653/v1/2021.findings-emnlp.340
%P 4051-4056

Markdown (Informal)

[Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event](https://aclanthology.org/2021.findings-emnlp.340) (Khanal & Caragea, Findings 2021)

• Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event (Khanal & Caragea, Findings 2021)

ACL

• Sarthak Khanal and Doina Caragea. 2021. Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4051–4056, Punta Cana, Dominican Republic. Association for Computational Linguistics.