GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks

• URL: http://arxiv.org/abs/2305.16663v2
• Date: Thu, 15 Jun 2023 02:43:12 GMT
• Title: GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks
• Authors: Xuming Hu, Aiwei Liu, Zeqi Tan, Xin Zhang, Chenwei Zhang, Irwin King, Philip S. Yu
• Abstract summary: We propose a dedicated augmentation technique for relational texts, named GDA, which uses two complementary modules to preserve both semantic consistency and syntax structures. Experimental results in three datasets under a low-resource setting showed that GDA could bring em 2.0% F1 improvements compared with no augmentation technique.
• Score: 81.51314139202152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Relation extraction (RE) tasks show promising performance in extracting relations from two entities mentioned in sentences, given sufficient annotations available during training. Such annotations would be labor-intensive to obtain in practice. Existing work adopts data augmentation techniques to generate pseudo-annotated sentences beyond limited annotations. These techniques neither preserve the semantic consistency of the original sentences when rule-based augmentations are adopted, nor preserve the syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations. In this work, we propose a dedicated augmentation technique for relational texts, named GDA, which uses two complementary modules to preserve both semantic consistency and syntax structures. We adopt a generative formulation and design a multi-tasking solution to achieve synergies. Furthermore, GDA adopts entity hints as the prior knowledge of the generative model to augment diverse sentences. Experimental results in three datasets under a low-resource setting showed that GDA could bring {\em 2.0\%} F1 improvements compared with no augmentation technique. Source code and data are available.

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