  The paradigm of pre-training followed by fine-tuning on downstream tasks has
become the mainstream method in natural language processing tasks. Although
pre-trained models have the advantage of generalization, their performance may
still vary significantly across different domain tasks. This is because the
data distribution in different domains varies. For example, the different parts
of the sentence 'He married Smt. Dipali Ghosh in 1947 and led a very happy
married life' may have different impact for downstream tasks. For similarity
calculations, words such as 'led' and 'life' are more important. On the other
hand, for sentiment analysis, the word 'happy' is crucial. This indicates that
different downstream tasks have different levels of sensitivity to sentence
components. Our starting point is to scale information of the model and data
according to the specifics of downstream tasks, enhancing domain information of
relevant parts for these tasks and reducing irrelevant elements for different
domain tasks, called SIFTER. In the experimental part, we use the SIFTER to
improve SimCSE by constructing positive sample pairs based on enhancing the
sentence stem and reducing the unimportant components in the sentence, and
maximize the similarity between three sentences. Similarly, SIFTER can improve
the gate mechanism of the LSTM model by short-circuiting the input gate of
important words so that the LSTM model remembers the important parts of the
sentence. Our experiments demonstrate that SIFTER outperforms the SimCSE and
LSTM baselines.
