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 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 topicdependent evidence detection.
More recently, neural networks have been applied to the task of topic-dependent evidence detection REF , improving the performance on a manually labelled corpus through the use of unsupervised data.
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining
ACL
Computer Science