The process of obtaining high quality labeled data for natural language understanding tasks is often slow, error-prone, complicated and expensive. With the vast usage of neural networks, this issue becomes more notorious since these networks require a large amount of labeled data to produce satisfactory results. We propose a methodology to blend high quality but scarce strong labeled data with noisy but abundant weak labeled data during the training of neural networks. Experiments in the context of topic-dependent evidence detection with two forms of weak labeled data show the advantages of the blending scheme. In addition, we provide a manually annotated data set for the task of topic-dependent evidence detection. We believe that blending weak and strong labeled data is a general notion that may be applicable to many language understanding tasks, and can especially assist researchers who wish to train a network but have a small amount of high quality labeled data for their task of interest.
@inproceedings{DBLP:conf/acl/ShnarchPDGHCAS18,
author = {Eyal Shnarch and
Carlos Alzate and
Lena Dankin and
Martin Gleize and
Yufang Hou and
Leshem Choshen and
Ranit Aharonov and
Noam Slonim},
editor = {Iryna Gurevych and
Yusuke Miyao},
title = {Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network
for Argumentation Mining},
booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational
Linguistics, {ACL} 2018, Melbourne, Australia, July 15-20, 2018, Volume
2: Short Papers},
pages = {599--605},
publisher = {Association for Computational Linguistics},
year = {2018},
url = {https://aclanthology.org/P18-2095/},
doi = {10.18653/V1/P18-2095},
timestamp = {Fri, 06 Aug 2021 01:00:00 +0200},
biburl = {https://dblp.org/rec/conf/acl/ShnarchPDGHCAS18.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
See the full reference list in the paper.