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        <title>ECG-QALM: Entity-Controlled Synthetic Text Generation using Contextual Q&amp;A for NER</title>
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        <namePart type="given">Karan</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T ECG-QALM: Entity-Controlled Synthetic Text Generation using Contextual Q&A for NER
%A Aggarwal, Karan
%A Jin, Henry
%A Ahmad, Aitzaz
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F aggarwal-etal-2023-ecg
%X 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.
%R 10.18653/v1/2023.findings-acl.349
%U https://aclanthology.org/2023.findings-acl.349
%U https://doi.org/10.18653/v1/2023.findings-acl.349
%P 5649-5660

Markdown (Informal)

[ECG-QALM: Entity-Controlled Synthetic Text Generation using Contextual Q&A for NER](https://aclanthology.org/2023.findings-acl.349) (Aggarwal et al., Findings 2023)

• ECG-QALM: Entity-Controlled Synthetic Text Generation using Contextual Q&A for NER (Aggarwal et al., Findings 2023)

ACL

• Karan Aggarwal, Henry Jin, and Aitzaz Ahmad. 2023. ECG-QALM: Entity-Controlled Synthetic Text Generation using Contextual Q&A for NER. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5649–5660, Toronto, Canada. Association for Computational Linguistics.