  We introduce a training method for both better word representation and
performance, which we call GROVER (Gradual Rumination On the Vector with
maskERs). The method is to gradually and iteratively add random noises to word
embeddings while training a model. GROVER first starts from conventional
training process, and then extracts the fine-tuned representations. Next, we
gradually add random noises to the word representations and repeat the training
process from scratch, but initialize with the noised word representations.
Through the re-training process, we can mitigate some noises to be compensated
and utilize other noises to learn better representations. As a result, we can
get word representations further fine-tuned and specialized on the task. When
we experiment with our method on 5 text classification datasets, our method
improves model performances on most of the datasets. Moreover, we show that our
method can be combined with other regularization techniques, further improving
the model performance.
