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        <title>Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup</title>
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        <namePart type="given">Jishnu</namePart>
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            <namePart type="given">Shruti</namePart>
            <namePart type="family">Rijhwani</namePart>
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            <namePart type="given">Jiangming</namePart>
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    <abstract>Distinguishing informative and actionable messages from a social media platform like Twitter is critical for facilitating disaster management. For this purpose, we compile a multilingual dataset of over 130K samples for multi-label classification of disaster-related tweets. We present a masking-based loss function for partially labelled samples and demonstrate the effectiveness of Manifold Mixup in the text domain. Our main model is based on Multilingual BERT, which we further improve with Manifold Mixup. We show that our model generalizes to unseen disasters in the test set. Furthermore, we analyze the capability of our model for zero-shot generalization to new languages. Our code, dataset, and other resources are available on Github.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup
%A Ray Chowdhury, Jishnu
%A Caragea, Cornelia
%A Caragea, Doina
%Y Rijhwani, Shruti
%Y Liu, Jiangming
%Y Wang, Yizhong
%Y Dror, Rotem
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F ray-chowdhury-etal-2020-cross
%X Distinguishing informative and actionable messages from a social media platform like Twitter is critical for facilitating disaster management. For this purpose, we compile a multilingual dataset of over 130K samples for multi-label classification of disaster-related tweets. We present a masking-based loss function for partially labelled samples and demonstrate the effectiveness of Manifold Mixup in the text domain. Our main model is based on Multilingual BERT, which we further improve with Manifold Mixup. We show that our model generalizes to unseen disasters in the test set. Furthermore, we analyze the capability of our model for zero-shot generalization to new languages. Our code, dataset, and other resources are available on Github.
%R 10.18653/v1/2020.acl-srw.39
%U https://aclanthology.org/2020.acl-srw.39
%U https://doi.org/10.18653/v1/2020.acl-srw.39
%P 292-298

Markdown (Informal)

[Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup](https://aclanthology.org/2020.acl-srw.39) (Ray Chowdhury et al., ACL 2020)

• Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup (Ray Chowdhury et al., ACL 2020)

ACL

• Jishnu Ray Chowdhury, Cornelia Caragea, and Doina Caragea. 2020. Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 292–298, Online. Association for Computational Linguistics.