Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks
Arijit Sehanobish, Kawshik Kannan, Nabila Abraham, Anasuya Das, Benjamin Odry
Abstract
Large pretrained Transformer-based language models like BERT and GPT have changed the landscape of Natural Language Processing (NLP). However, fine tuning such models still requires a large number of training examples for each target task, thus annotating multiple datasets and training these models on various downstream tasks becomes time consuming and expensive. In this work, we propose a simple extension of the Prototypical Networks for few-shot text classification. Our main idea is to replace the class prototypes by Gaussians and introduce a regularization term that encourages the examples to be clustered near the appropriate class centroids. Experimental results show that our method outperforms various strong baselines on 13 public and 4 internal datasets. Furthermore, we use the class distributions as a tool for detecting potential out-of-distribution (OOD) data points during deployment.
BibTeX
@inproceedings{sehanobish-etal-2022-meta,
title = "Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks",
author = "Sehanobish, Arijit and
Kannan, Kawshik and
Abraham, Nabila and
Das, Anasuya and
Odry, Benjamin",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.34/",
doi = "10.18653/v1/2022.emnlp-industry.34",
pages = "332--347"
}