ECG-QALM: Entity-Controlled Synthetic Text Generation using Contextual Q&A for NER
Karan Aggarwal, Henry Jin, Aitzaz Ahmad · 2023
Named Entity Recognition (NER) state-ofthe-art methods requires high-quality labeled datasets.Issues such as scarcity of labeled data, under-representation of entities, and privacy concerns with using sensitive data for training, can be significant barriers.Generating synthetic data to train models is a promising solution to mitigate these problems.We propose ECG-QALM, a contextual question and answering approach using pre-trained language models to synthetically generate entitycontrolled text.Generated text is then used to augment small labeled datasets for downstream NER tasks.We evaluate our method on two publicly available datasets.We find ECG-QALM is capable of producing full text samples with desired entities appearing in a controllable way, while retaining sentence coherence closest to the real world data.Evaluations on NER tasks show significant improvements (75% -140%) in low-labeled data regimes.