{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/domain-adaptation-with-adversarial-training","title":"Domain Adaptation with Adversarial Training and Graph Embeddings","arxiv_id":"1805.05151","date":"2018-05-14","proceeding":"ACL 2018 7","authors":["Firoj Alam","Shafiq Joty","Muhammad Imran"],"abstract":"The success of deep neural networks (DNNs) is heavily dependent on the\navailability of labeled data. However, obtaining labeled data is a big\nchallenge in many real-world problems. In such scenarios, a DNN model can\nleverage labeled and unlabeled data from a related domain, but it has to deal\nwith the shift in data distributions between the source and the target domains.\nIn this paper, we study the problem of classifying social media posts during a\ncrisis event (e.g., Earthquake). For that, we use labeled and unlabeled data\nfrom past similar events (e.g., Flood) and unlabeled data for the current\nevent. We propose a novel model that performs adversarial learning based domain\nadaptation to deal with distribution drifts and graph based semi-supervised\nlearning to leverage unlabeled data within a single unified deep learning\nframework. Our experiments with two real-world crisis datasets collected from\nTwitter demonstrate significant improvements over several baselines.","url_abs":"http://arxiv.org/abs/1805.05151v1","url_pdf":"http://arxiv.org/pdf/1805.05151v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"domain-adaptation-with-adversarial-training","repo_url":"https://github.com/firojalam/domain-adaptation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.05151","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}