{"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/rotom-a-meta-learned-data-augmentation","title":"Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and Beyond","arxiv_id":null,"date":"2021-06-01","proceeding":"Proceedings of the International Conference on Management of Data (SIGMOD) 2021 6","authors":["Zhengjie Miao","Yuliang Li and Xiaolan Wang"],"abstract":"Deep Learning revolutionizes almost all fields of computer science including data management. However, the demand for high-quality training data is slowing down deep neural nets' wider adoption. To this end, data augmentation (DA), which generates more labeled examples from existing ones, becomes a common technique. Meanwhile, the risk of creating noisy examples and the large space of hyper-parameters make DA less attractive in practice. We introduce Rotom, a multi-purpose data augmentation framework for a range of data management and mining tasks including entity matching, data cleaning, and text classification. Rotom features InvDA, a new DA operator that generates natural yet diverse augmented examples by formulating DA as a seq2seq task. The key technical novelty of Rotom is a meta-learning framework that automatically learns a policy for combining examples from different DA operators, whereby combinatorially reduces the hyper-parameters space. Our experimental results show that Rotom effectively improves a model's performance by combining multiple DA operators, even when applying them individually does not yield performance improvement. With this strength, Rotom outperforms the state-of-the-art entity matching and data cleaning systems in the low-resource settings as well as two recently proposed DA techniques for text classification.","url_abs":"https://dl.acm.org/doi/abs/10.1145/3448016.3457258","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3448016.3457258","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":"rotom-a-meta-learned-data-augmentation","repo_url":"https://github.com/megagonlabs/rotom","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"entity-resolution","task_name":"Entity Resolution"},{"task_slug":"management","task_name":"Management"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}