{"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/a-multi-task-semantic-decomposition-framework","title":"A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER","arxiv_id":"2308.14533","date":"2023-08-28","proceeding":null,"authors":["Guanting Dong","Zechen Wang","Jinxu Zhao","Gang Zhao","Daichi Guo","Dayuan Fu","Tingfeng Hui","Chen Zeng","Keqing He","Xuefeng Li","LiWen Wang","Xinyue Cui","Weiran Xu"],"abstract":"The objective of few-shot named entity recognition is to identify named entities with limited labeled instances. Previous works have primarily focused on optimizing the traditional token-wise classification framework, while neglecting the exploration of information based on NER data characteristics. To address this issue, we propose a Multi-Task Semantic Decomposition Framework via Joint Task-specific Pre-training (MSDP) for few-shot NER. Drawing inspiration from demonstration-based and contrastive learning, we introduce two novel pre-training tasks: Demonstration-based Masked Language Modeling (MLM) and Class Contrastive Discrimination. These tasks effectively incorporate entity boundary information and enhance entity representation in Pre-trained Language Models (PLMs). In the downstream main task, we introduce a multi-task joint optimization framework with the semantic decomposing method, which facilitates the model to integrate two different semantic information for entity classification. Experimental results of two few-shot NER benchmarks demonstrate that MSDP consistently outperforms strong baselines by a large margin. Extensive analyses validate the effectiveness and generalization of MSDP.","url_abs":"https://arxiv.org/abs/2308.14533v1","url_pdf":"https://arxiv.org/pdf/2308.14533v1.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":"a-multi-task-semantic-decomposition-framework","repo_url":"https://github.com/dongguanting/msdp-fewshot-ner","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"few-shot-ner","task_name":"Few-shot NER"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"masked-language-modeling","task_name":"Masked Language Modeling"},{"task_slug":"cg","task_name":"NER"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"few-shot-ner","task_name":"few-shot-ner"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-ner-on-few-nerd-inter","task":"Few-shot NER","dataset":"Few-NERD (INTER)","model":"MSDP","rank_in_archive_order":1,"of":13,"metrics":{"10 way 1~2 shot":"69.78±0.31","10 way 5~10 shot":"81.50±0.71","5 way 1~2 shot":"76.86±0.22","5 way 5~10 shot":"84.78±0.69"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-ner-on-few-nerd-intra","task":"Few-shot NER","dataset":"Few-NERD (INTRA)","model":"MSDP","rank_in_archive_order":4,"of":13,"metrics":{"10 way 1~2 shot":"47.13±0.69","10 way 5~10 shot":"64.69±0.51","5 way 1~2 shot":"56.35±0.28","5 way 5~10 shot":"66.80±0.78"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}