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.

本研究提出了一种名为 GDA 的专用增强技术，该技术使用两个互补的模块来保留句子的语义一致性和语法结构，在低资源环境下取得了 2.0％的 F1 改进。