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- W4385571279 abstract "Named Entity Recognition (NER) state-of-the-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 entity-controlled 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." @default.
- W4385571279 created "2023-08-05" @default.
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- W4385571279 date "2023-01-01" @default.
- W4385571279 modified "2023-09-24" @default.
- W4385571279 title "ECG-QALM: Entity-Controlled Synthetic Text Generation using Contextual Q&A for NER" @default.
- W4385571279 doi "https://doi.org/10.18653/v1/2023.findings-acl.349" @default.
- W4385571279 hasPublicationYear "2023" @default.
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