DiaFrame: A Framework for Understanding Bengali Dialects in Human-AI Collaborative Creative Writing Spaces

Azmine Toushik Wasi, Taki Hasan Rafi, Dong-Kyu Chae

Abstract: Preserving linguistic dialects is paramount for preserving cultural identity, fostering diversity, and maintaining cultural prestige. In the context of Bengali, the language exhibits religious distinctions through its two major dialects: the West Bengal (Hindu majority) and Bangladesh (Muslim majority) dialects, deeply rooted in the historical evolution of Bangla. Understanding and respecting these dialect-based nuances is crucial for Human-AI collaborative tools to generate culturally appropriate content and effectively assist human writers without limiting their creativity. To address these challenges, we introduce DiaFrame, a comprehensive framework tailored for understanding and tuning dialects in human-AI collaborative creative writing spaces, focusing on Bengali. It integrates advanced components such as active learning, real-time processing of human feedback, memory management, and contextual understanding to enhance user experience and ensure culturally sensitive content generation. Our contributions include providing a holistic end-to-end solution that enables the AI model to actively learn, adapt, and generate content aligned with user preferences and dialectical variations, ultimately fostering effective collaboration between humans and AI in creative writing endeavors.