  Fine-tuning large pre-trained language models (LLMs) on particular datasets
is a commonly employed strategy in Natural Language Processing (NLP)
classification tasks. However, this approach usually results in a loss of
models generalizability. In this paper, we present a framework that allows for
maintaining generalizability, and enhances the performance on the downstream
task by utilizing task-specific context attribution. We show that a linear
transformation of the text representation from any transformer model using the
task-specific concept operator results in a projection onto the latent concept
space, referred to as context attribution in this paper. The specific concept
operator is optimized during the supervised learning stage via novel loss
functions. The proposed framework demonstrates that context attribution of the
text representation for each task objective can improve the capacity of the
discriminator function and thus achieve better performance for the
classification task. Experimental results on three datasets, namely HateXplain,
IMDB reviews, and Social Media Attributions, illustrate that the proposed model
attains superior accuracy and generalizability. Specifically, for the
non-fine-tuned BERT on the HateXplain dataset, we observe 8% improvement in
accuracy and 10% improvement in F1-score. Whereas for the IMDB dataset,
fine-tuned state-of-the-art XLNet is outperformed by 1% for both accuracy and
F1-score. Furthermore, in an out-of-domain cross-dataset test, DistilBERT
fine-tuned on the IMDB dataset in conjunction with the proposed model improves
the F1-score on the HateXplain dataset by 7%. For the Social Media Attributions
dataset of YouTube comments, we observe 5.2% increase in F1-metric. The
proposed framework is implemented with PyTorch and provided open-source on
GitHub.
