用自动标注数据提升数据库实体识别准确率
Database Entity Recognition with Data Augmentation and Deep Learning
- 基于SQL自动生成自然语言查询，扩充训练数据
- 在多个基准上精度与召回率均提升10%以上
- 适合做文本转SQL任务的研究者和开发者
本文针对自然语言查询中的数据库实体识别（DB-ER）挑战，提出三项关键贡献：（1）从主流文本转SQL基准构建人工标注的DB-ER基准数据集；（2）设计一种新颖的数据增强方法，利用已有文本转SQL基准中对应SQL查询自动标注自然语言查询；（3）基于T5架构构建专用实体识别模型，分别针对序列标注与词元分类两个下游任务进行微调。实验对比了两种先进命名实体识别模型，结果表明本模型在精确率和召回率上均表现更优。消融实验显示，数据增强使精确率与召回率提升超过10%，而T5主干微调进一步带来5%-10%的性能增益。
原文摘要 · Abstract (English)
This paper addresses the challenge of Database Entity Recognition (DB-ER) in Natural Language Queries (NLQ). We present several key contributions to advance this field: (1) a human-annotated benchmark for DB-ER task, derived from popular text-to-sql benchmarks, (2) a novel data augmentation procedure that leverages automatic annotation of NLQs based on the corresponding SQL queries which are available in popular text-to-SQL benchmarks, (3) a specialized language model based entity recognition model using T5 as a backbone and two down-stream DB-ER tasks: sequence tagging and token classification for fine-tuning of backend and performing DB-ER respectively. We compared our DB-ER tagger with two state-of-the-art NER taggers, and observed better performance in both precision and recall for our model. The ablation evaluation shows that data augmentation boosts precision and recall by over 10%, while fine-tuning of the T5 backbone boosts these metrics by 5-10%.
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