  In this paper, we propose a dictionary screening method for embedding
compression in text classification tasks. The key purpose of this method is to
evaluate the importance of each keyword in the dictionary. To this end, we
first train a pre-specified recurrent neural network-based model using a full
dictionary. This leads to a benchmark model, which we then use to obtain the
predicted class probabilities for each sample in a dataset. Next, to evaluate
the impact of each keyword in affecting the predicted class probabilities, we
develop a novel method for assessing the importance of each keyword in a
dictionary. Consequently, each keyword can be screened, and only the most
important keywords are reserved. With these screened keywords, a new dictionary
with a considerably reduced size can be constructed. Accordingly, the original
text sequence can be substantially compressed. The proposed method leads to
significant reductions in terms of parameters, average text sequence, and
dictionary size. Meanwhile, the prediction power remains very competitive
compared to the benchmark model. Extensive numerical studies are presented to
demonstrate the empirical performance of the proposed method.
