Database Entity Recognition with Data Augmentation and Deep Learning
It addresses DB-ER for natural language query processing, with incremental improvements in performance.
This paper tackles the problem of Database Entity Recognition (DB-ER) in Natural Language Queries by introducing a new benchmark, a data augmentation method, and a specialized T5-based model, achieving over 10% improvements in precision and recall through data augmentation and 5-10% gains from fine-tuning.
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%.