Modeling Contextual Understanding for Conversational Agents Development: A Systematic Review of Recent Advances and Challenges
DOI:
https://doi.org/10.4314/njt.2025.4521Keywords:
Contextual Modeling, Conversational Agents, Multi-turn Dialogue, Natural Language Understanding, Deep Learning TechniquesAbstract
Accurate capture of contextual information is a critical requirement for developing conversational agents capable of sustaining coherent and relevant multi-turn dialogues. By effectively understanding the context of an ongoing conversation, conversational agents can improve user experience through consistent dialogue flow, memory retention, and personalized responses. However, challenges persist in designing scalable and efficient context-aware models. This systematic review examines recent advancements, methodologies, and current challenges in context modeling for conversational agents, with a focus on techniques for enhancing coherence, contextual retention, and user-centered interactions. To conduct this review, a structured search was performed across multiple databases using terms such as contextual modeling, multi-turn dialogue, and contextual understanding in conversational AI. Studies meeting inclusion criteria were categorized based on their methodologies such as memory networks, attention mechanisms, graph-based approaches, and transformer-based models. Results reveal a strong reliance on deep learning architectures, particularly transformers, which have improved context retention across complex dialogues. Memory-augmented models, attention mechanisms, and graph-based approaches also show promise in handling context continuity and user-specific personalization. Despite these advancements, significant challenges remain in computational efficiency, scalability, and ethical considerations, especially regarding data privacy and user trust. In conclusion, while the field has made notable progress in enhancing context modeling, further work is required to address efficiency, scalability, and ethical implications. Future research in areas like dynamic context adaptation, multi-modal context integration, and session continuity holds potential for developing more sophisticated and responsible conversational agents.
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