IJCAI 2022poster1 citations
Data-Efficient Algorithms and Neural Natural Language Processing: Applications in the Healthcare Domain
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.
Speech & Natural Language Processing (SNLP): GeneralMachine Learning (ML): General
BibTeX
@inproceedings{ijcai2022p839,
title = {Data-Efficient Algorithms and Neural Natural Language Processing: Applications in the Healthcare Domain},
author = {Shim, Heereen},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {5873--5874},
year = {2022},
month = {7},
note = {Doctoral Consortium},
doi = {10.24963/ijcai.2022/839},
url = {https://doi.org/10.24963/ijcai.2022/839},
}