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Delta training: Simple Semi Supervised Text Classification using Pretrained Word Embeddings

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Figure

Figure 1: The flow of ∆-training framework. Mrand and Memb are ensembled classifiers using randomly initial-ized word embeddings and pretrained word embeddings, respectively
Table 1: The data split information of text classificationdatasets. We use 1% of the training data and removethe labels of the remaining training data, using them asunlabeled data.
Figure 2: The training curve and the ratio of differently predicted label during ∆-training
Figure 5: The performance of ∆-training with respect to initial training data size. ∆-training performs well indifferent training data size and is more useful when the training data is scarce.

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