# Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining

Authors: Eyal Shnarch, Carlos Alzate, Lena Dankin, Martin Gleize, Yufang Hou, Leshem Choshen, Ranit Aharonov, Noam Slonim
Venue: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, {ACL} 2018, Melbourne, Australia, July 15-20, 2018, Volume 2: Short Papers (2018)

## Abstract
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.

## Links
- DOI: https://doi.org/10.18653/V1/P18-2095
- ACL Anthology: https://aclanthology.org/P18-2095/
- Semantic Scholar: https://www.semanticscholar.org/paper/51873028

## How to cite
@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}
}