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

2026-03-21

AI Summary

Sentiment analysis remains challenging in the presence of irrelevant contextual interference. This work proposes the Non-Differentiable Transformer (NDT), which introduces a Concept Multiplexing (ConPlex) mechanism that dispenses with conventional attention subtraction operations. Instead, NDT employs only learnable positive weights to additively fuse distinct attention maps generated by multiple independent attention heads, thereby encouraging both specialization and collaboration among components. This purely additive strategy achieves competitive performance across several sentiment analysis benchmarks, demonstrating its effectiveness and novelty in modeling sentiment-related information.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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 positive weights, learned during training, to ensure constructive combination of these specialized attention perspectives. This design choice explores positive only integration, though our broader framework also shows promise with less constrained linear combinations involving both positive and negative weights. Our model computes attention via this positively weighted sum of multiple distinct attention maps. This allows the model to constructively integrate diverse signals and potentially capture more complex contextual relationships. Competitive performance is achieved by the proposed model for Sentiment Analysis while tested on multiple datasets. We conclude by presenting our results, challenges and future research agenda in this important area of research.

Problem

• Sentiment Analysis
• Transformer
• Attention Mechanism
• Natural Language Processing
• Emotion Recognition

Innovation

• Non-Differential Transformer
• Concept Multiplexing
• Additive Attention
• Positive Weight Integration
• Sentiment Analysis