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One appealing property\nof such systems is their generality, as excellent performance can be achieved\nwith a unified architecture and without task-specific feature engineering.\nHowever, it is unclear if such systems can be used for tasks without large\namounts of training data. In this paper we explore the problem of transfer\nlearning for neural sequence taggers, where a source task with plentiful\nannotations (e.g., POS tagging on Penn Treebank) is used to improve performance\non a target task with fewer available annotations (e.g., POS tagging for\nmicroblogs). We examine the effects of transfer learning for deep hierarchical\nrecurrent networks across domains, applications, and languages, and show that\nsignificant improvement can often be obtained. These improvements lead to\nimprovements over the current state-of-the-art on several well-studied tasks.","url_abs":"http://arxiv.org/abs/1703.06345v1","url_pdf":"http://arxiv.org/pdf/1703.06345v1.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":"transfer-learning-for-sequence-tagging-with","repo_url":"https://github.com/kimiyoung/transfer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"transfer-learning-for-sequence-tagging-with","repo_url":"https://github.com/jiesutd/NCRFpp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"transfer-learning-for-sequence-tagging-with","repo_url":"https://github.com/jiesutd/PyTorchSeqLabel","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"transfer-learning-for-sequence-tagging-with","repo_url":"https://github.com/jiesutd/yato","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"pos","task_name":"POS"},{"task_slug":"pos-tagging","task_name":"POS Tagging"},{"task_slug":"part-of-speech-tagging","task_name":"Part-Of-Speech Tagging"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/named-entity-recognition-ner-on-conll-2003","task":"Named Entity Recognition (NER)","dataset":"CoNLL 2003 (English)","model":"Yang et al.","rank_in_archive_order":65,"of":73,"metrics":{"F1":"91.26"},"uses_additional_data":false},{"leaderboard":"/sota/part-of-speech-tagging-on-penn-treebank","task":"Part-Of-Speech Tagging","dataset":"Penn Treebank","model":"Yang et al.","rank_in_archive_order":10,"of":20,"metrics":{"Accuracy":"97.55"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1703.06345","atlas_url":"https://app.syntology.ai/?focus=1703.06345","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.06345"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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