MANAGING CONTEXTUAL SHIFTS: PRAGMATICS AND SEMANTICS IN AI DIALOGUE SYSTEMS FOR PAKISTANI LANGUAGE DOCUMENTATION

Abstract

This paper investigates the challenges and opportunities of developing context-aware AI dialogue systems for effective language documentation in Pakistan's linguistically diverse environment. The point of focus in the study is the need of pragmatics and semantics in the functions of the AI systems, because these models are facing so many problems when it comes to contextual shifts, dialectical variations, and nuances. Current transformer-based models like BERT or GPT do tend to be good at semantic interpretation, nonetheless they seem to lack the capability to process pragmatic components like honorifics, politeness strategies and region-specific speech rules that a lot of Pakistani language require. It then reviews prior tools such as Google translate and special models for Urdu and Punjabi that struggle to adapt to fast evolving text exchanges, dialectical differences, and the sociolinguistic nuances of Pakistan. Most existing evaluation frameworks focus on languages such as Arabic, Hindi and Bengali without much consideration for Pakistan’s regionally important needs. Through culturally enriched training datasets, context sensitive algorithms and metrics suited to local dialects, AI systems can be more competent at documenting — and adapting to — the dialectic between semantics and pragmatics. This work supports for a holistic approach to AI development to guarantee accurate, comprehensive and contextually aware language documentation for Pakistan's multilingual communities.