Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data
Michael A. Hedderich, Dietrich Klakow · 2018
Manually labeled corpora are expensive to create and often not available for lowresource 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.