{"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/an-enhanced-span-based-decomposition-method","title":"An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling","arxiv_id":"2109.13023","date":"2021-09-27","proceeding":"NAACL 2022 7","authors":["Peiyi Wang","Runxin Xu","Tianyu Liu","Qingyu Zhou","Yunbo Cao","Baobao Chang","Zhifang Sui"],"abstract":"Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain. Recently, the metric-based meta-learning framework has been recognized as a promising approach for FSSL. However, most prior works assign a label to each token based on the token-level similarities, which ignores the integrality of named entities or slots. To this end, in this paper, we propose ESD, an Enhanced Span-based Decomposition method for FSSL. ESD formulates FSSL as a span-level matching problem between test query and supporting instances. Specifically, ESD decomposes the span matching problem into a series of span-level procedures, mainly including enhanced span representation, class prototype aggregation and span conflicts resolution. Extensive experiments show that ESD achieves the new state-of-the-art results on two popular FSSL benchmarks, FewNERD and SNIPS, and is proven to be more robust in the nested and noisy tagging scenarios. Our code is available at https://github.com/Wangpeiyi9979/ESD.","url_abs":"https://arxiv.org/abs/2109.13023v3","url_pdf":"https://arxiv.org/pdf/2109.13023v3.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":"an-enhanced-span-based-decomposition-method","repo_url":"https://github.com/wangpeiyi9979/esd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"few-shot-ner","task_name":"Few-shot NER"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"slot-filling","task_name":"Slot Filling"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"},{"task_slug":"slot-filling-1","task_name":"slot-filling"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-ner-on-few-nerd-inter","task":"Few-shot NER","dataset":"Few-NERD (INTER)","model":"ESD","rank_in_archive_order":8,"of":13,"metrics":{"10 way 1~2 shot":"52.16±0.79","10 way 5~10 shot":"64.00±0.43","5 way 1~2 shot":"59.29±1.25","5 way 5~10 shot":"69.06±0.80"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-ner-on-few-nerd-intra","task":"Few-shot NER","dataset":"Few-NERD (INTRA)","model":"ESD","rank_in_archive_order":10,"of":13,"metrics":{"10 way 1~2 shot":"30.00±0.70","10 way 5~10 shot":"42.15±2.60","5 way 1~2 shot":"36.08±1.60","5 way 5~10 shot":"52.14±1.50"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.13023","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}