@inproceedings{surana-etal-2023-cassi,
    title = "{CASSI}: Contextual and Semantic Structure-based Interpolation Augmentation for Low-Resource {NER}",
    author = "Surana, Tanmay  and
      Ho, Thi-Nga  and
      Tun, Kyaw  and
      Chng, Eng Siong",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.findings-emnlp.651/",
    doi = "10.18653/v1/2023.findings-emnlp.651",
    pages = "9729--9742",
    abstract = "While text augmentation methods have been successful in improving performance in the low-resource setting, they suffer from annotation corruption for a token-level task like NER. Moreover, existing methods cannot reliably add context diversity to the dataset, which has been shown to be crucial for low-resource NER. In this work, we propose Contextual and Semantic Structure-based Interpolation (CASSI), a novel augmentation scheme that generates high-quality contextually diverse augmentations while avoiding annotation corruption by structurally combining a pair of semantically similar sentences to generate a new sentence while maintaining semantic correctness and fluency. To accomplish this, we generate candidate augmentations by performing multiple dependency parsing-based exchanges in a pair of semantically similar sentences that are filtered via scoring with a pretrained Masked Language Model and a metric to promote specificity. Experiments show that CASSI consistently outperforms existing methods at multiple low resource levels, in multiple languages, and for noisy and clean text."
}