  In this paper, we study bidirectional LSTM network for the task of text
classification using both supervised and semi-supervised approaches. Several
prior works have suggested that either complex pretraining schemes using
unsupervised methods such as language modeling (Dai and Le 2015; Miyato, Dai,
and Goodfellow 2016) or complicated models (Johnson and Zhang 2017) are
necessary to achieve a high classification accuracy. However, we develop a
training strategy that allows even a simple BiLSTM model, when trained with
cross-entropy loss, to achieve competitive results compared with more complex
approaches. Furthermore, in addition to cross-entropy loss, by using a
combination of entropy minimization, adversarial, and virtual adversarial
losses for both labeled and unlabeled data, we report state-of-the-art results
for text classification task on several benchmark datasets. In particular, on
the ACL-IMDB sentiment analysis and AG-News topic classification datasets, our
method outperforms current approaches by a substantial margin. We also show the
generality of the mixed objective function by improving the performance on
relation extraction task.
