Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data

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...

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Bibliographic Details
Published in:arXiv.org 2018-07
Main Authors: Hedderich, Michael A, Klakow, Dietrich
Format: Article
Language:English
Subjects:
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Neural networks
Noise
Performance enhancement
Simulation
Online Access:Get full text
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Summary: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.
ISSN:2331-8422