Cross-domain sentiment classification aims to predict the sentiment tendency in unlabeled
target domain data using labeled source-domain data. The wide range of data sources has …

In this paper, we present a denoising sequence-to-sequence (seq2seq) autoencoder via
contrastive learning for abstractive text summarization. Our model adopts a standard …

FinTech lending (eg, micro-lending) has played a significant role in facilitating financial
inclusion. It has reduced processing times and costs, enhanced the user experience, and …

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics
learned from the source domain to predict the sentiment of reviews in the unlabeled target …

Achieving inclusiveness in decision-making is of utmost importance for advancing social
justice and mitigating inequality. This is especially critical in high-stakes decisions, including …

Contrastive learning can reduce the impact of ex-posure bias associated with training using
maximum likelihood estimation, which aims to pull together positive samples to increase the …

Significant improvements in the accuracy of Neural Networks (NNs) have been observed for
a wide range of problems, often achieved by highly over-parameterized models. Despite the …

Automatic text summarization is the task of generating a precise text snippet to capture the
most relevant and critical information from an input document. It is one of the central …