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        <title>Domain Adaptation with Adversarial Training and Graph Embeddings</title>
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        <namePart type="given">Firoj</namePart>
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    <name type="personal">
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
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    <abstract>The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. However, obtaining labeled data is a big challenge in many real-world problems. In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data distributions between the source and the target domains. In this paper, we study the problem of classifying social media posts during a crisis event (e.g., Earthquake). For that, we use labeled and unlabeled data from past similar events (e.g., Flood) and unlabeled data for the current event. We propose a novel model that performs adversarial learning based domain adaptation to deal with distribution drifts and graph based semi-supervised learning to leverage unlabeled data within a single unified deep learning framework. Our experiments with two real-world crisis datasets collected from Twitter demonstrate significant improvements over several baselines.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1099</identifier>
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%0 Conference Proceedings
%T Domain Adaptation with Adversarial Training and Graph Embeddings
%A Alam, Firoj
%A Joty, Shafiq
%A Imran, Muhammad
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F alam-etal-2018-domain
%X The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. However, obtaining labeled data is a big challenge in many real-world problems. In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data distributions between the source and the target domains. In this paper, we study the problem of classifying social media posts during a crisis event (e.g., Earthquake). For that, we use labeled and unlabeled data from past similar events (e.g., Flood) and unlabeled data for the current event. We propose a novel model that performs adversarial learning based domain adaptation to deal with distribution drifts and graph based semi-supervised learning to leverage unlabeled data within a single unified deep learning framework. Our experiments with two real-world crisis datasets collected from Twitter demonstrate significant improvements over several baselines.
%R 10.18653/v1/P18-1099
%U https://aclanthology.org/P18-1099
%U https://doi.org/10.18653/v1/P18-1099
%P 1077-1087

Markdown (Informal)

[Domain Adaptation with Adversarial Training and Graph Embeddings](https://aclanthology.org/P18-1099) (Alam et al., ACL 2018)

• Domain Adaptation with Adversarial Training and Graph Embeddings (Alam et al., ACL 2018)

ACL

• Firoj Alam, Shafiq Joty, and Muhammad Imran. 2018. Domain Adaptation with Adversarial Training and Graph Embeddings. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1077–1087, Melbourne, Australia. Association for Computational Linguistics.