Inceptive Transformers: Enhancing Contextual Representations through Multi-Scale Feature Learning Across Domains and Languages

• URL: http://arxiv.org/abs/2505.20496v1
• Date: Mon, 26 May 2025 19:59:22 GMT
• Title: Inceptive Transformers: Enhancing Contextual Representations through Multi-Scale Feature Learning Across Domains and Languages
• Authors: Asif Shahriar, Rifat Shahriyar, M Saifur Rahman,
• Abstract summary: We introduce textitInceptive Transformer, a modular and lightweight architecture that enriches transformer-based token representations.<n>Our model is designed to balance local and global dependencies by dynamically weighting tokens based on their relevance to a particular task.
• Score: 2.98683507969764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional transformer models typically compress the information from all tokens in a sequence into a single \texttt{[CLS]} token to represent global context-- an approach that can lead to information loss in tasks requiring localized or hierarchical cues. In this work, we introduce \textit{Inceptive Transformer}, a modular and lightweight architecture that enriches transformer-based token representations by integrating a multi-scale feature extraction module inspired by inception networks. Our model is designed to balance local and global dependencies by dynamically weighting tokens based on their relevance to a particular task. Evaluation across a diverse range of tasks including emotion recognition (both English and Bangla), irony detection, disease identification, and anti-COVID vaccine tweets classification shows that our models consistently outperform the baselines by 1\% to 14\% while maintaining efficiency. These findings highlight the versatility and cross-lingual applicability of our method for enriching transformer-based representations across diverse domains.

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