Domain Adaptation with Adversarial Training and Graph Embeddings
<p dir="ltr">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 doma...
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2018
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| _version_ | 1864513509783502848 |
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| author | Firoj Alam (14158866) |
| author2 | Shafiq Joty (4576078) Muhammad Imran (282621) |
| author2_role | author author |
| author_facet | Firoj Alam (14158866) Shafiq Joty (4576078) Muhammad Imran (282621) |
| author_role | author |
| dc.creator.none.fl_str_mv | Firoj Alam (14158866) Shafiq Joty (4576078) Muhammad Imran (282621) |
| dc.date.none.fl_str_mv | 2018-07-01T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.18653/v1/p18-1099 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/conference_contribution/Domain_Adaptation_with_Adversarial_Training_and_Graph_Embeddings/25919455 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning Language, communication and culture Linguistics Labeled Data Unlabeled Data Domain Shift Data Distribution Social Media Posts Crisis Event Classification Adversarial Learning |
| dc.title.none.fl_str_mv | Domain Adaptation with Adversarial Training and Graph Embeddings |
| dc.type.none.fl_str_mv | Text Conference contribution info:eu-repo/semantics/publishedVersion text conference object |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See conference contribution on publisher's website: <a href="https://dx.doi.org/10.18653/v1/p18-1099" target="_blank">https://dx.doi.org/10.18653/v1/p18-1099</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_6275b52e27cfcd3be0ae252a8b8a292f |
| identifier_str_mv | 10.18653/v1/p18-1099 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25919455 |
| publishDate | 2018 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Domain Adaptation with Adversarial Training and Graph EmbeddingsFiroj Alam (14158866)Shafiq Joty (4576078)Muhammad Imran (282621)Information and computing sciencesArtificial intelligenceMachine learningLanguage, communication and cultureLinguisticsLabeled DataUnlabeled DataDomain ShiftData DistributionSocial Media PostsCrisis Event ClassificationAdversarial Learning<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See conference contribution on publisher's website: <a href="https://dx.doi.org/10.18653/v1/p18-1099" target="_blank">https://dx.doi.org/10.18653/v1/p18-1099</a></p>2018-07-01T03:00:00ZTextConference contributioninfo:eu-repo/semantics/publishedVersiontextconference object10.18653/v1/p18-1099https://figshare.com/articles/conference_contribution/Domain_Adaptation_with_Adversarial_Training_and_Graph_Embeddings/25919455CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/259194552018-07-01T03:00:00Z |
| spellingShingle | Domain Adaptation with Adversarial Training and Graph Embeddings Firoj Alam (14158866) Information and computing sciences Artificial intelligence Machine learning Language, communication and culture Linguistics Labeled Data Unlabeled Data Domain Shift Data Distribution Social Media Posts Crisis Event Classification Adversarial Learning |
| status_str | publishedVersion |
| title | Domain Adaptation with Adversarial Training and Graph Embeddings |
| title_full | Domain Adaptation with Adversarial Training and Graph Embeddings |
| title_fullStr | Domain Adaptation with Adversarial Training and Graph Embeddings |
| title_full_unstemmed | Domain Adaptation with Adversarial Training and Graph Embeddings |
| title_short | Domain Adaptation with Adversarial Training and Graph Embeddings |
| title_sort | Domain Adaptation with Adversarial Training and Graph Embeddings |
| topic | Information and computing sciences Artificial intelligence Machine learning Language, communication and culture Linguistics Labeled Data Unlabeled Data Domain Shift Data Distribution Social Media Posts Crisis Event Classification Adversarial Learning |