CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Distantly Supervised Relation Extraction in Federated Settings
Sui DB(隋典伯)1,2; Yubo Chen1,2; Kang Liu1,2; Jun Zhao1,2
Conference NameFindings of the Association for Computational Linguistics: EMNLP 2021
Conference Date2021
Conference PlacePunta Cana, Dominican Republic

In relation extraction, distant supervision is widely used to automatically label a large-scale training dataset by aligning a knowledge base with unstructured text. Most existing studies in this field have assumed there is a great deal of centralized unstructured text. However, in practice, texts are usually distributed on different platforms and cannot be centralized due to privacy restrictions. Therefore, it is worthwhile to investigate distant supervision in the federated learning paradigm, which decouples the training of the model from the need for direct access to raw texts. However, overcoming label noise of distant supervision becomes more difficult in federated settings, because texts containing the same entity pair scatter around different platforms. In this paper, we propose a federated denoising framework to suppress label noise in federated settings. The key of this framework is a multiple instance learning based denoising method that is able to select reliable sentences via cross-platform collaboration. Various experiments on New York Times dataset and miRNA gene regulation relation dataset demonstrate the effectiveness of the proposed method.

Document Type会议论文
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, CAS
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Sui DB,Yubo Chen,Kang Liu,et al. Distantly Supervised Relation Extraction in Federated Settings[C],2021.
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