ML Anthology

Data-Efficient Algorithms and Neural Natural Language Processing: Applications in the Healthcare Domain

Heereen Shim
IJCAI 2022 pp. 5873-5874
doi:10.24963/IJCAI.2022/839 /ijcai/2022/shim2022ijcai-data/

Abstract

Recently proposed pre-trained language models can be easily fine-tuned to a wide range of downstream tasks. However, fine-tuning requires a large training set. This PhD project introduces novel natural language processing (NLP) use cases in the healthcare domain where obtaining a large training dataset is difficult and expensive. To this end, we propose data-efficient algorithms to fine-tune NLP models in low-resource settings and validate their effectiveness. We expect the outcomes of this PhD project could contribute to the NLP research and low-resource application domains.

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Cite

Text

Shim. "Data-Efficient Algorithms and Neural Natural Language Processing: Applications in the Healthcare Domain." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/839

Markdown

[Shim. "Data-Efficient Algorithms and Neural Natural Language Processing: Applications in the Healthcare Domain." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/shim2022ijcai-data/) doi:10.24963/IJCAI.2022/839

BibTeX

@inproceedings{shim2022ijcai-data,
  title     = {{Data-Efficient Algorithms and Neural Natural Language Processing: Applications in the Healthcare Domain}},
  author    = {Shim, Heereen},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {5873-5874},
  doi       = {10.24963/IJCAI.2022/839},
  url       = {https://mlanthology.org/ijcai/2022/shim2022ijcai-data/}
}