[2603.20704] NDT: Non-Differential Transformer and Its Application to Sentiment Analysis

Computer Science > Information Retrieval arXiv:2603.20704 (cs) [Submitted on 21 Mar 2026] Title:NDT: Non-Differential Transformer and Its Application to Sentiment Analysis Authors:Soudeep Ghoshal, Himanshu Buckchash, Sarita Paudel, Rubén Ruiz-Torrubiano View a PDF of the paper titled NDT: Non-Differential Transformer and Its Application to Sentiment Analysis, by Soudeep Ghoshal and 3 other authors View PDF HTML (experimental) Abstract:From customer feedback to social media, understanding human sentiment in text is central to how machines can interact meaningfully with people. However, despite notable progress, accurately capturing sentiment remains a challenging task, which continues to motivate further research in this area. To this end, we introduce Non-Differential Transformer (NDT). It is inspired by (but in contrast to) the state-of-the-art Differential Transformer (DT) model. While standard Transformers can struggle with irrelevant context, the sota DT model uses attention map subtraction, potentially for noise cancellation. We explore an alternative motivation, hypothesizing that benefits may arise from enabling different attention components to specialize on distinct concepts within the text, similar to multiplexing information channels or mixture models, rather than primarily canceling noise via subtraction. Guided by this concept-multiplexing (ConPlex) view, the specific architecture presented in this paper employs a purely additive strategy. It uses only positiv...