Distantly Supervised Relation Extraction in Federated Settings

Dianbo Sui, Yubo Chen, Kang Liu, Jun Zhao

Abstract
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.
Anthology ID:
2021.findings-emnlp.52
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
569–583
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.52
DOI:
10.18653/v1/2021.findings-emnlp.52
Bibkey:
Cite (ACL):
Dianbo Sui, Yubo Chen, Kang Liu, and Jun Zhao. 2021. Distantly Supervised Relation Extraction in Federated Settings. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 569–583, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Distantly Supervised Relation Extraction in Federated Settings (Sui et al., Findings 2021)
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PDF:
https://preview.aclanthology.org/auto-file-uploads/2021.findings-emnlp.52.pdf
Code
 DianboWork/FedDS
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