  Pre-trained language models allowed us to process downstream tasks with the
help of fine-tuning, which aids the model to achieve fairly high accuracy in
various Natural Language Processing (NLP) tasks. Such easily-downloaded
language models from various websites empowered the public users as well as
some major institutions to give a momentum to their real-life application.
However, it was recently proven that models become extremely vulnerable when
they are backdoor attacked with trigger-inserted poisoned datasets by malicious
users. The attackers then redistribute the victim models to the public to
attract other users to use them, where the models tend to misclassify when
certain triggers are detected within the training sample. In this paper, we
will introduce a novel improved textual backdoor defense method, named MSDT,
that outperforms the current existing defensive algorithms in specific
datasets. The experimental results illustrate that our method can be effective
and constructive in terms of defending against backdoor attack in text domain.
Code is available at https://github.com/jcroh0508/MSDT.
