{"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/latent-multi-task-architecture-learning","title":"Latent Multi-task Architecture Learning","arxiv_id":"1705.08142","date":"2017-05-23","proceeding":null,"authors":["Sebastian Ruder","Joachim Bingel","Isabelle Augenstein","Anders Søgaard"],"abstract":"Multi-task learning (MTL) allows deep neural networks to learn from related\ntasks by sharing parameters with other networks. In practice, however, MTL\ninvolves searching an enormous space of possible parameter sharing\narchitectures to find (a) the layers or subspaces that benefit from sharing,\n(b) the appropriate amount of sharing, and (c) the appropriate relative weights\nof the different task losses. Recent work has addressed each of the above\nproblems in isolation. In this work we present an approach that learns a latent\nmulti-task architecture that jointly addresses (a)--(c). We present experiments\non synthetic data and data from OntoNotes 5.0, including four different tasks\nand seven different domains. Our extension consistently outperforms previous\napproaches to learning latent architectures for multi-task problems and\nachieves up to 15% average error reductions over common approaches to MTL.","url_abs":"http://arxiv.org/abs/1705.08142v3","url_pdf":"http://arxiv.org/pdf/1705.08142v3.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":"latent-multi-task-architecture-learning","repo_url":"https://github.com/sebastianruder/sluice-networks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"latent-multi-task-architecture-learning","repo_url":"https://github.com/lzzhaha/Multi-task-learning-for-Hate-Speech-Detection-Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1705.08142","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}