{"type":"ScholarlyArticle","title":"Improving Pseudo Labels with Global-Local Denoising Framework for Cross-lingual Named Entity Recognition","authors":[{"name":"Zhuojun Ding","slug":"zhuojun-ding","given":"Zhuojun","family":"Ding","affiliation":"Cognitive Computing and Intelligent Information Processing (CCIIP) Laboratory , School of Computer Science and Technology , Huazhong University of Science and Technology; Joint Laboratory of HUST and Pingan Property & Casualty Research (HPL)"},{"name":"Wei Wei","slug":"wei-wei","given":"Wei","family":"Wei","affiliation":"Cognitive Computing and Intelligent Information Processing (CCIIP) Laboratory , School of Computer Science and Technology , Huazhong University of Science and Technology; Joint Laboratory of HUST and Pingan Property & Casualty Research (HPL)"},{"name":"Xiaoye Qu","slug":"xiaoye-qu","given":"Xiaoye","family":"Qu","affiliation":"Cognitive Computing and Intelligent Information Processing (CCIIP) Laboratory , School of Computer Science and Technology , Huazhong University of Science and Technology; Joint Laboratory of HUST and Pingan Property & Casualty Research (HPL)"},{"name":"Dangyang Chen","slug":"dangyang-chen","given":"Dangyang","family":"Chen","affiliation":"Ping An Property & Casualty Insurance Company of China, Ltd."}],"abstract":"Cross-lingual named entity recognition (NER) aims to train an NER model for the target language leveraging only labeled source language data and unlabeled target language data. Prior approaches either perform label projection on translated source language data or employ a source model to assign pseudo labels for target language data and train a target model on these pseudo-labeled data to generalize to the target language. However, these automatic labeling procedures inevitably introduce noisy labels, thus leading to a performance drop. In this paper, we propose a Global-Local Denoising framework (GLoDe) for cross-lingual NER. Specifically, GLoDe introduces a progressive denoising strategy to rectify incorrect pseudo labels by leveraging both global and local distribution information in the semantic space. The refined pseudo-labeled target language data significantly improves the model's generalization ability. Moreover, previous methods only consider improving the model with language-agnostic features, however, we argue that target language-specific features are also important and should never be ignored. To this end, we employ a simple auxiliary task to achieve this goal. Experimental results on two benchmark datasets with six target languages demonstrate that our proposed GLoDe significantly outperforms current state-of-the-art methods.","doi":"10.24963/ijcai.2024/691","url":"https://ijcai.org/proceedings/2024/691","pdf":"https://ijcai.org/proceedings/2024/0691.pdf","markdown":"https://ijcai.org/proceedings/2024/691.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/691","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":691,"pages":{"first":6252,"last":6260},"keywords":["Natural Language Processing: NLP: Information extraction","Natural Language Processing: NLP: Named entities"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2021] Weile Chen, Huiqiang Jiang, Qianhui Wu, Börje Karlsson, and Yi Guan. 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