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Domain Adaptation with Adversarial Training and Graph Embeddings

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submitted on 2024-05-29, 07:33 and posted on 2024-05-29, 12:49 authored by Firoj Alam, Shafiq Joty, Muhammad Imran

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.

Other Information

Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
See article on publisher's website:



  • English


Association for Computational Linguistics

Publication Year

  • 2018

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU