{"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/automated-labeling-of-bugs-and-tickets-using","title":"Automated labeling of bugs and tickets using attention-based mechanisms in recurrent neural networks","arxiv_id":"1807.02892","date":"2018-07-08","proceeding":null,"authors":["Volodymyr Lyubinets","Taras Boiko","Deon Nicholas"],"abstract":"We explore solutions for automated labeling of content in bug trackers and\ncustomer support systems. In order to do that, we classify content in terms of\nseveral criteria, such as priority or product area. In the first part of the\npaper, we provide an overview of existing methods used for text classification.\nThese methods fall into two categories - the ones that rely on neural networks\nand the ones that don't. We evaluate results of several solutions of both\nkinds. In the second part of the paper we present our own recurrent neural\nnetwork solution based on hierarchical attention paradigm. It consists of\nseveral Hierarchical Attention network blocks with varying Gated Recurrent Unit\ncell sizes and a complementary shallow network that goes alongside. Lastly, we\nevaluate above-mentioned methods when predicting fields from two datasets -\nArch Linux bug tracker and Chromium bug tracker. Our contributions include a\ncomprehensive benchmark between a variety of methods on relevant datasets; a\nnovel solution that outperforms previous generation methods; and two new\ndatasets that are made public for further research.","url_abs":"http://arxiv.org/abs/1807.02892v1","url_pdf":"http://arxiv.org/pdf/1807.02892v1.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":"automated-labeling-of-bugs-and-tickets-using","repo_url":"https://github.com/Forethought-Technologies/ieee-dsmp-2018-paper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}