Learning from Unlabelled Data for Clinical Semantic Textual Similarity

Yuxia Wang, Karin Verspoor, Timothy Baldwin


Abstract
Domain pretraining followed by task fine-tuning has become the standard paradigm for NLP tasks, but requires in-domain labelled data for task fine-tuning. To overcome this, we propose to utilise domain unlabelled data by assigning pseudo labels from a general model. We evaluate the approach on two clinical STS datasets, and achieve r= 0.80 on N2C2-STS. Further investigation reveals that if the data distribution of unlabelled sentence pairs is closer to the test data, we can obtain better performance. By leveraging a large general-purpose STS dataset and small-scale in-domain training data, we obtain further improvements to r= 0.90, a new SOTA.
Anthology ID:
2020.clinicalnlp-1.25
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Venues:
ClinicalNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
227–233
Language:
URL:
https://www.aclweb.org/anthology/2020.clinicalnlp-1.25
DOI:
10.18653/v1/2020.clinicalnlp-1.25
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.clinicalnlp-1.25.pdf