Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data
Machine Learning
2018-07-24 v2 Computation and Language
Machine Learning
Abstract
Manually labeled corpora are expensive to create and often not available for low-resource languages or domains. Automatic labeling approaches are an alternative way to obtain labeled data in a quicker and cheaper way. However, these labels often contain more errors which can deteriorate a classifier's performance when trained on this data. We propose a noise layer that is added to a neural network architecture. This allows modeling the noise and train on a combination of clean and noisy data. We show that in a low-resource NER task we can improve performance by up to 35% by using additional, noisy data and handling the noise.
Cite
@article{arxiv.1807.00745,
title = {Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data},
author = {Michael A. Hedderich and Dietrich Klakow},
journal= {arXiv preprint arXiv:1807.00745},
year = {2018}
}
Comments
In Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP 2018