@inproceedings{chaudhry-etal-2022-reducing,
title = "Reducing Inference Time of Biomedical {NER} Tasks using Multi-Task Learning",
author = "Chaudhry, Mukund Chaudhry and
Kazmi, Arman and
Jatav, Shashank and
Verma, Akhilesh and
Samal, Vishal and
Paul, Kristopher and
Modi, Ashutosh",
editor = "Akhtar, Md. Shad and
Chakraborty, Tanmoy",
booktitle = "Proceedings of the 19th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2022",
address = "New Delhi, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.icon-main.16",
pages = "116--122",
abstract = "Recently, fine-tuned transformer-based models (e.g., PubMedBERT, BioBERT) have shown the state-of-the-art performance of a number of BioNLP tasks, such as Named Entity Recognition (NER). However, transformer-based models are complex and have millions of parameters, and, consequently, are relatively slow during inference. In this paper, we address the time complexity limitations of the BioNLP transformer models. In particular, we propose a Multi-Task Learning based framework for jointly learning three different biomedical NER tasks. Our experiments show a reduction in inference time by a factor of three without any reduction in prediction accuracy.",
}
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<abstract>Recently, fine-tuned transformer-based models (e.g., PubMedBERT, BioBERT) have shown the state-of-the-art performance of a number of BioNLP tasks, such as Named Entity Recognition (NER). However, transformer-based models are complex and have millions of parameters, and, consequently, are relatively slow during inference. In this paper, we address the time complexity limitations of the BioNLP transformer models. In particular, we propose a Multi-Task Learning based framework for jointly learning three different biomedical NER tasks. Our experiments show a reduction in inference time by a factor of three without any reduction in prediction accuracy.</abstract>
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%0 Conference Proceedings
%T Reducing Inference Time of Biomedical NER Tasks using Multi-Task Learning
%A Chaudhry, Mukund Chaudhry
%A Kazmi, Arman
%A Jatav, Shashank
%A Verma, Akhilesh
%A Samal, Vishal
%A Paul, Kristopher
%A Modi, Ashutosh
%Y Akhtar, Md. Shad
%Y Chakraborty, Tanmoy
%S Proceedings of the 19th International Conference on Natural Language Processing (ICON)
%D 2022
%8 December
%I Association for Computational Linguistics
%C New Delhi, India
%F chaudhry-etal-2022-reducing
%X Recently, fine-tuned transformer-based models (e.g., PubMedBERT, BioBERT) have shown the state-of-the-art performance of a number of BioNLP tasks, such as Named Entity Recognition (NER). However, transformer-based models are complex and have millions of parameters, and, consequently, are relatively slow during inference. In this paper, we address the time complexity limitations of the BioNLP transformer models. In particular, we propose a Multi-Task Learning based framework for jointly learning three different biomedical NER tasks. Our experiments show a reduction in inference time by a factor of three without any reduction in prediction accuracy.
%U https://aclanthology.org/2022.icon-main.16
%P 116-122
Markdown (Informal)
[Reducing Inference Time of Biomedical NER Tasks using Multi-Task Learning](https://aclanthology.org/2022.icon-main.16) (Chaudhry et al., ICON 2022)
ACL
- Mukund Chaudhry Chaudhry, Arman Kazmi, Shashank Jatav, Akhilesh Verma, Vishal Samal, Kristopher Paul, and Ashutosh Modi. 2022. Reducing Inference Time of Biomedical NER Tasks using Multi-Task Learning. In Proceedings of the 19th International Conference on Natural Language Processing (ICON), pages 116–122, New Delhi, India. Association for Computational Linguistics.