{"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/training-a-neural-network-in-a-low-resource","title":"Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data","arxiv_id":"1807.00745","date":"2018-07-02","proceeding":"WS 2018 7","authors":["Michael A. Hedderich","Dietrich Klakow"],"abstract":"Manually labeled corpora are expensive to create and often not available for\nlow-resource languages or domains. Automatic labeling approaches are an\nalternative way to obtain labeled data in a quicker and cheaper way. However,\nthese labels often contain more errors which can deteriorate a classifier's\nperformance when trained on this data. We propose a noise layer that is added\nto a neural network architecture. This allows modeling the noise and train on a\ncombination of clean and noisy data. We show that in a low-resource NER task we\ncan improve performance by up to 35% by using additional, noisy data and\nhandling the noise.","url_abs":"http://arxiv.org/abs/1807.00745v2","url_pdf":"http://arxiv.org/pdf/1807.00745v2.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":"training-a-neural-network-in-a-low-resource","repo_url":"https://github.com/uds-lsv/Training-a-Neural-Network-in-a-Low-Resource-Setting-on-Automatically-Annotated-Noisy-Data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"cg","task_name":"NER"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.00745","atlas_url":"https://app.syntology.ai/?focus=1807.00745","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}