  We present a novel retrofitting method to induce emotion aspects into
pre-trained language models (PLMs) such as BERT and RoBERTa. Our method updates
pre-trained network weights using contrastive learning so that the text
fragments exhibiting similar emotions are encoded nearby in the representation
space, and the fragments with different emotion content are pushed apart. While
doing so, it also ensures that the linguistic knowledge already present in PLMs
is not inadvertently perturbed. The language models retrofitted by our method,
i.e., BERTEmo and RoBERTaEmo, produce emotion-aware text representations, as
evaluated through different clustering and retrieval metrics. For the
downstream tasks on sentiment analysis and sarcasm detection, they perform
better than their pre-trained counterparts (about 1% improvement in F1-score)
and other existing approaches. Additionally, a more significant boost in
performance is observed for the retrofitted models over pre-trained ones in
few-shot learning setting.
